An energy hub inter-cluster distributed collaborative control method and system containing energy storage

Abstract

A distributed collaborative control method and system for energy hub clusters with energy storage, belonging to the field of energy storage cluster control, is disclosed. It includes: constructing a power network model reflecting the coupling mechanism between frequency, voltage, and active and reactive power; constructing a heating network model reflecting the coupling mechanism between port pressure and pipeline flow; designing primary droop control to initially allocate electrical and thermal power according to capacity ratio based on local deviations; designing secondary power control based on a consensus algorithm, introducing frequency recovery error terms and power allocation error terms to achieve frequency recovery and precise power allocation; designing a heating network average pressure observer to collaboratively calculate average pressure and correct pressure deviations through a PI controller; and executing the above steps in real time during load changes to achieve dynamic collaborative control. This invention is applicable to distributed autonomous control among multiple energy hub clusters with energy storage, effectively promoting the automation and integrated control of electrical and thermal energy, and improving the integration and intelligence level of energy systems.

Description

Technical Field

[0001] This invention belongs to the field of distributed energy storage control technology, and more specifically, relates to a distributed collaborative control method and system for energy hub clusters containing energy storage. Background Technology

[0002] Microgrids have emerged as an innovative energy management model. They can overcome the bottlenecks of traditional power systems, providing greater optimization space for renewable energy consumption. Currently, the main challenge facing renewable energy consumption is the contradiction between the randomness and indirectness of wind and solar power and the real-time power balance requirements of the grid. Because electricity is difficult to store on a large scale, this leads to frequent wind and solar curtailment. Microgrids, by converting electricity into more easily stored energy forms such as natural gas, hydrogen, and heat, and utilizing the longer time constants of heat and gas networks to achieve complementary strengths and weaknesses of various energy sources, provide more flexible resources for grid regulation.

[0003] In rural power distribution networks, the grid structure is relatively weak, and there are significant differences in the spatial and temporal distribution of energy demand and power supply equipment, making the supply-demand balance easily disrupted. Existing power supply models are insufficient to meet the increasingly diversified energy needs of rural industries and users. Therefore, developing microgrid technology is of great significance for improving the power supply capacity and quality of rural power grids, as well as enhancing rural power self-sufficiency. Simultaneously, the multi-energy complementarity of microgrids can promote the consumption of various energy sources and enhance the rural power grid's capacity to support distributed renewable energy.

[0004] However, current control methods for microgrids mostly focus on the modeling and scheduling of energy hubs, with relatively little research on automated control methods. A key challenge in microgrid control research lies in the analysis and modeling of the dynamic characteristics of multi-energy flow networks. The operating characteristics of power grids, heating networks, and gas networks differ significantly, and a unified, standardized modeling method for multi-energy flow networks is currently lacking. Furthermore, traditional centralized control methods suffer from high computational costs and significant communication failure risks when dealing with complex microgrid topologies. Therefore, there is an urgent need to develop a distributed collaborative control method among energy hub clusters to achieve efficient collaborative operation of energy hubs within a microgrid, improving energy quality and operational economy. This research has significant practical implications. Summary of the Invention

[0005] In response to the shortcomings and improvement needs of existing technologies, this invention provides a distributed collaborative control method and system for energy hub clusters with energy storage, aiming to promote the efficient collaborative operation of various energy hubs in microgrids and improve energy supply quality and operational economy.

[0006] To achieve the above objectives, the present invention provides a distributed collaborative control method for energy hub clusters including energy storage, comprising:

[0007] Modeling of power network characteristics, modeling of heating network characteristics, and distributed collaborative control methods for energy hub clusters.

[0008] The modeling of power network characteristics is used to reflect the coupling mechanism between parameters such as frequency and voltage of the power network and the active and reactive power transmitted by the network. In this model, active power-frequency and reactive power-voltage exhibit strong coupling characteristics.

[0009] The modeling of the heating network characteristics is used to reflect the coupling mechanism between port pressure and pipeline flow in the heating network. In this model, port pressure and pipeline flow exhibit strong coupling characteristics.

[0010] The distributed collaborative control method for energy hub clusters containing energy storage is used to coordinate the reasonable allocation of electrical and thermal power output of each energy hub cluster containing energy storage according to its capacity.

[0011] Preferably, the modeling of the power network characteristics focuses on the strong coupling characteristics of active power and frequency, and the relationship between the frequency and output power of each energy hub's grid connection point can be represented by the characteristics of synchronous machine nodes:

[0012]

[0013] Where δ is the nodal power angle, ω and ω s These are the per-unit and reference values ​​of the node angular frequency, respectively, T. j P is the equivalent time constant. m and P e These represent the input mechanical power and the output electromagnetic power, respectively, with D representing the damping coefficient.

[0014] Beneficial Effects: Modern power systems generally employ high-voltage AC grids, whose inherent transmission characteristics dictate strong coupling between active power and phase angle, and reactive power and voltage. Specifically, active power is transmitted from nodes with leading phase to those with lagging phase, while reactive power is transmitted from nodes with high voltage to those with low voltage. The phase angle difference is actually affected by the node frequency, thus active power output is indirectly influenced by the node frequency. Energy hubs, acting as bridges for energy conversion and transformation, are linked together by users' active power demands. Therefore, the grid portion of microgrids focuses on active power output; that is, the control strategy for the microgrid grid portion is designed around the strong coupling characteristics of active power and frequency to simplify the grid modeling process.

[0015] Preferably, the energy hub outputs electrical and thermal energy using a hierarchical distributed control structure. The primary control uses droop-based control logic to distribute power proportionally to the capacity of the energy hub, while the secondary control uses a consistency error term to restore network parameters.

[0016]

[0017]

[0018] Beneficial effects: The hierarchical distributed control structure offers high reliability, specifically manifested in the independence of primary control and the fault tolerance of the distributed architecture. Primary control employs droop control, eliminating reliance on communication networks and relying solely on local information for initial power allocation. Even if the communication network fails or experiences delays, the system maintains basic power allocation functionality, ensuring stable operation. Furthermore, the secondary control utilizes a consensus-based distributed control structure, reducing dependence on a central controller and avoiding the risk of single points of failure. Even if a node or communication link fails, the system can still maintain operation through the collaborative efforts of other nodes.

[0019] Preferably, the secondary control of the output power of the energy hub adopts a leader-led consensus algorithm, which restores the frequency deviation and ensures accurate distribution of output power by introducing a frequency recovery error term and a power allocation error term, as expressed as:

[0020]

[0021]

[0022] Beneficial effects: The frequency recovery error term employs a leader-based consensus algorithm, similar to the dominant generator frequency regulation method in secondary frequency regulation strategies. When the load changes, the frequency deviation of the energy hub will be corrected through the leader node, and other nodes will exchange frequency information through the communication network to follow the leader node in restoring the frequency. Compared to frequency regulation methods that introduce deviations from the reference frequency at each node, this method ensures stable frequency regulation and avoids repeated power adjustments due to inconsistent frequency deviations among nodes.

[0023] Preferably, the primary control of the thermal energy output of the energy hub adopts a droop control architecture based on consistency. By introducing a proportional allocation error term, the correction of the droop control output thermal power when the outlet pressure of the energy hub is inconsistent is realized in the consistency iteration, as expressed as:

[0024]

[0025]

[0026] Beneficial effects: Compared to introducing a pressure difference in the transmission pipeline into the droop control equation. This additional feature, based on a consistent droop control architecture, can eliminate the power distribution imbalance caused by differences in EH outlet pressure with less information, requiring only consistent iterations between energy hubs; however, introducing this additional feature to correct the measured energy hub outlet pressure requires additional measurement of the volumetric flow velocity in the transmission pipeline. and resistance These two physical quantities are actually not easy to measure.

[0027] Preferably, the secondary control of the heat energy output from the energy hub employs an average pressure observer. Through the coordinated efforts of each energy hub, the average voltage of the heating network is constrained within a reasonable range, as expressed below:

[0028]

[0029]

[0030] Beneficial effects: The droop-based control method can only limit the pressure change within a certain range and cannot eliminate the pressure deviation. Therefore, an average pressure observer was designed based on the consistency theory. It can calculate the average pressure of the heating network in a distributed manner through the cooperative communication of adjacent agents. The pressure deviation term is then superimposed on the output controller of the energy hub through a PI controller, thereby constraining the pressure of each transmission pipeline.

[0031] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0032] 1) Improve the stability of microgrid operation: Through a hierarchical distributed control strategy based on droop control and consensus algorithm, this invention can effectively coordinate the power distribution among multiple energy hubs to ensure the stability of grid frequency and heating network pressure, and improve the stability of microgrid under different operating conditions.

[0033] 2) Optimize energy allocation: Adhesion control is used to achieve initial power allocation, and a consensus algorithm is used to adaptively correct power allocation errors, ensuring that the output power of each energy hub is allocated proportionally to its capacity, thereby optimizing the energy utilization efficiency within the microgrid.

[0034] 3) Improve power supply quality: Through frequency recovery and pressure constraint strategies, this invention can effectively maintain the key parameters of the power grid and heating network near their rated values, reduce voltage fluctuations and frequency deviations, and improve the power supply quality of the microgrid.

[0035] 4) Adaptability to complex topologies: The distributed control structure reduces the dependence on the central controller, avoids the risk of single point of failure, and can adapt to complex microgrid topologies, supporting flexible expansion and dynamic adjustment of the system.

[0036] Overall, this invention provides a theoretical basis and solution for distributed collaborative control among multiple energy hub clusters containing energy storage, which is of great significance for promoting the automated and integrated control of electric and thermal energy and improving the integration and intelligence level of energy systems. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the microgrid topology according to an embodiment of the present invention.

[0038] Figure 2 This is a global curve showing the output power of each energy hub in an embodiment of the present invention.

[0039] Figure 3 These are locally magnified curves of the output power of each energy hub in the embodiments of the present invention around 150s.

[0040] Figure 4 These are locally magnified curves of the output power of each energy hub in the embodiments of the present invention around 300s.

[0041] Figure 5 These are the global frequency curves of various energy hubs in embodiments of the present invention.

[0042] Figure 6 These are magnified curves of the energy hub frequencies around 150s in various embodiments of the present invention.

[0043] Figure 7 These are magnified curves of the energy hub frequencies around 300s in various embodiments of the present invention.

[0044] Figure 8 These are the output thermal power curves of various energy hubs in embodiments of the present invention.

[0045] Figure 9 This is the average pressure observation curve and the outlet pressure curve of an embodiment of the present invention.

[0046] Figure 10 These are the actual export pressure curves of various energy hubs in the embodiments of the present invention.

[0047] Figure 11 This is a flowchart illustrating a distributed collaborative control method among energy hub clusters containing energy storage, as described in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0049] Please see Figure 11 This invention provides a distributed collaborative control method for energy hub clusters including energy storage, comprising:

[0050] Step 1: Construct a power network model, which describes the coupling mechanism between the frequency and voltage of the power network and the active and reactive power transmitted through the lines;

[0051] The construction of the power network model in step 1 specifically includes: a single power source is connected to the AC bus through an inductive branch, and the output apparent power is:

[0052]

[0053] Where S is the apparent power, This is the power supply output voltage. For the common bus voltage, Line impedance;

[0054] From the above formula, the output active power P and reactive power Q are respectively:

[0055] ;

[0056] .

[0057] For AC high-voltage power grids, the line reactance X is much greater than the resistance R, therefore the voltage phase angles at both ends of the line are not significantly different, approximately: , Therefore, active frequency and reactive power have strong coupling characteristics, which can be expressed as:

[0058] ;

[0059] .

[0060] The power network model focuses on the strong coupling characteristics of active power and frequency. The relationship between the frequency and output power at each energy hub's grid connection point can be represented by the characteristics of synchronous machine nodes.

[0061]

[0062] Where δ is the nodal power angle, ω and ω s These are the per-unit and reference values ​​of the node angular frequency, respectively, T. j P is the equivalent time constant. m and P e These represent the input mechanical power and the output electromagnetic power, respectively, with D representing the damping coefficient.

[0063] Because modern power systems generally use AC high-voltage grids, their inherent transmission characteristics determine the strong coupling between active power and phase angle, and reactive power and voltage. Specifically, active power is transmitted from nodes with leading phase to nodes with lagging phase, and reactive power is transmitted from nodes with high voltage to nodes with low voltage. The phase angle difference is actually affected by the node frequency, so active power output is indirectly affected by the node frequency. Energy hubs, as bridges for energy conversion and transformation, are coupled together by users' active power demands. Therefore, the grid portion of microgrids focuses on active power output; that is, the control strategy for the microgrid grid portion is designed around the strong coupling characteristics of active power and frequency to simplify the grid modeling process.

[0064] Step 2: Construct a heating network model, which describes the coupling mechanism between port pressure and pipeline flow in the heating network;

[0065] Step 2 involves constructing a heating network model, specifically including: Under flow control mode, the flow rate in the transmission pipes of the heating network and the port pressure have strong coupling characteristics, represented as:

[0066] ;

[0067] Among them, S l S is the equivalent resistance of the load pipeline. t =S OP +S IP The total resistance of the transmission pipeline, including the resistance S of the water supply pipeline. OP Return water pipe resistance S IP L hn Let N be the heat output of energy hub n, N be the number of energy hubs, and P be the outlet pressure of the energy hub. in For the inlet pressure of the energy hub, V t This indicates the volumetric flow rate of the transmission pipeline. The temperature loss of the load pipeline is represented by c, which represents the specific heat capacity of the heat transfer medium, and ρ is the density of the heat transfer medium.

[0068] The pressure difference at the inlet and outlet of each energy hub is affected by both heat load and output heat power. The output heat power L of each energy hub is... hn With the flow rate V of the transmission pipeline t One-to-one correspondence Without control commands, it will not change, but changes in heat load will cause changes in the resistance of the hot water pipes on the load side, as shown below:

[0069]

[0070] Among them, S1~S M This indicates the pipe resistance corresponding to each heat load.

[0071] Step 3: Based on the power network model and heating network model, design a primary droop control strategy for each energy hub to initially allocate electrical or thermal power according to the local frequency or pressure deviation and the capacity ratio.

[0072] In step 3, the control of the electrical output of each energy hub adopts a hierarchical distributed control structure. The primary control adopts a droop-based control logic, as follows:

[0073] ;

[0074] ;

[0075] Among them, L ei and L eNi f represents the actual and rated power output of the i-th energy hub, respectively. i Let f be the measured frequency value of the i-th energy hub, and f be the frequency rating. N = 50Hz, k pi Let be the droop coefficient of the electrical droop control equation for the i-th energy hub.

[0076] The control of the thermal energy output from each energy hub adopts a hierarchical distributed control structure. The primary control uses a consistency-based droop control logic, expressed as:

[0077] ;

[0078] ;

[0079] Among them, L hi and L hNi S represents the actual and rated output thermal power of the i-th energy hub, respectively. hi and S hj The maximum thermal power output of the i-th and j-th energy hubs, i.e., thermal power capacity, are given by k. qi p is the droop coefficient of the thermal droop control equation for the i-th energy hub. i and p Ni Let u be the measured and rated pressure of the outlet pipeline of the i-th energy hub. hi R represents the power distribution error term. h This is the gain coefficient for power distribution error.

[0080] Step 4: Based on the consensus algorithm, design the secondary control strategy for each energy hub, utilize the communication network to exchange state information, introduce frequency recovery error term and power allocation error term, correct the primary control results, and realize frequency recovery and accurate power allocation;

[0081] In step 4, the secondary control uses a consensus algorithm to introduce a frequency error correction term, expressed as:

[0082] ;

[0083] ;

[0084] Among them, c fi For the frequency recovery error term, c ei S is the power distribution error term. ei For the maximum electrical power output of the i-th energy hub, i.e., the electrical power capacity, g i The gain is the difference between the node's actual frequency and its rated frequency. For the leader node, g i =1, all follower nodes are 0, a ij Let a be an element of the adjacency matrix of nodes i and j. When there is communication between nodes i and j, a ij =1, otherwise, a ij = 0.

[0085] The frequency recovery error term employs a leader-based consensus algorithm, similar to the dominant generator frequency regulation method in a secondary frequency regulation strategy. When the load changes, the frequency deviation of the energy hub is corrected through the leader node, and other nodes exchange frequency information through the communication network, thereby following the leader node to recover the frequency. Compared to frequency regulation methods that introduce deviations from the reference frequency at each node, this method ensures stable frequency regulation and prevents repeated power adjustments due to inconsistent frequency deviations among nodes.

[0086] Step 5: Based on the consensus algorithm, design a heating network average pressure observer. Each energy hub collaboratively calculates the heating network average pressure, and a PI controller corrects the pressure deviation to ensure the stability of the heating network pressure.

[0087] In step 5, an average pressure observer for the heating network of the energy hub is constructed based on the consensus algorithm, namely:

[0088]

[0089] In the formula, The average heat network outlet pressure observed at the i-th energy hub;

[0090] Compared to introducing the pressure difference of the transmission pipeline into the droop control equation This additional feature, based on a consistent droop control architecture, can eliminate the power distribution imbalance caused by differences in EH outlet pressure with less information, requiring only consistent iterations between energy hubs; however, introducing this additional feature to correct the measured energy hub outlet pressure requires additional measurement of the volumetric flow velocity in the transmission pipeline. and resistance These two physical quantities are actually not easy to measure.

[0091] Compare the average pressure observation values ​​with the regional heating network pressure reference values. By making comparisons, the pressure deviation is obtained, and then a pressure deviation correction amount is generated through a PI controller. As shown in the following formula:

[0092]

[0093] Among them, G i (s) is the transfer function of the PI controller, u pi This indicates the pressure deviation correction term.

[0094] The droop-based control method can only limit the pressure change within a certain range and cannot eliminate the pressure deviation. Therefore, an average pressure observer was designed based on the consistency theory. It can calculate the average pressure of the heating network in a distributed manner through the cooperative communication of adjacent agents. The pressure deviation term is then superimposed on the output controller of the energy hub through a PI controller, thereby constraining the pressure of each transmission pipeline.

[0095] The hierarchical distributed control structure boasts high reliability, manifested in the independence of primary control and the fault tolerance of the distributed architecture. Primary control employs droop control, eliminating reliance on communication networks and relying solely on local information for initial power allocation. Even with communication network failures or delays, the system maintains basic power allocation functionality, ensuring stable operation. Furthermore, secondary control utilizes a consensus-based distributed control structure, reducing dependence on a central controller and avoiding single points of failure. Even if a node or communication link fails, the system can maintain operation through the collaborative efforts of other nodes.

[0096] Step 6: When the electrical and thermal loads change, execute steps 3 to 5 in real time to achieve dynamic and coordinated control of each energy hub.

[0097] Figure 1 The effectiveness of the proposed method is verified using a microgrid system containing multiple energy flows (electricity and heat) as an example. This microgrid has a typical topology comprising three energy hubs (EHs). The EHs communicate in a ring topology, with EH1 serving as the leader node for frequency recovery control. Each EH includes basic energy conversion equipment to achieve secondary distribution of input electrical energy from the energy supply side, natural gas, to the energy output side, and thermal energy. The electrical and thermal power capacities of each energy hub are shown in Table 1. Time-domain simulations were performed using MATLAB / Simulink.

[0098] Table 1. Electrical and thermal power capacity of energy hubs including energy storage

[0099]

[0100] The simulation was set as follows: at the initial moment, the electrical load and thermal load were 3500kW and 1750kW respectively. At 150s, the electrical load increased by 1050kW and the thermal load increased by 630kW. At 300s, the electrical load decreased by 1750kW and the thermal load decreased by 1050kW.

[0101] Under the above operating conditions, the global curves of the output power of each energy hub are as follows: Figure 2 As shown, the locally magnified curves of the output power around 150s and 300s are respectively as follows: Figure 3 , Figure 4 As shown. The global frequency curves for each energy hub are as follows. Figure 5 As shown, the locally magnified curves of frequencies around 150s and 300s are respectively as follows: Figure 6 , Figure 7 As shown.

[0102] It can be seen that the proposed power grid control strategy can not only achieve the proportional distribution of the power demanded by the load among the energy hubs, but also restore the frequency of each energy hub to the rated value of 50Hz. The power grid regains stability after a transient process of about 1 second, and the dynamic process is relatively short.

[0103] Taking a 150s increase in electrical load as an example, the specific control process is described as follows: When the electrical load increases, the frequency drops. Each energy hub rapidly increases its active power output according to the drooping equation to compensate for the power deficit, but this is still insufficient to restore the frequency. Simultaneously, the two designed error controllers also activate. When the frequency error controller detects a frequency drop, it immediately transmits the frequency error to the leader node (EH1), prompting the leader node to increase its active power rating to track the reference frequency. Other nodes follow the leader, increasing their own active power ratings, thereby restoring the frequency to 50Hz. As shown in the diagram, the leader node experiences the smallest frequency change and recovers to 50Hz the fastest, a natural consequence determined by the frequency recovery control sequence. The proportional distribution of active power is further regulated by the power distribution error controller. The increase in active power rating introduced by frequency recovery is precisely redistributed under the influence of the proportional distribution error term. This completes the power redistribution and frequency restoration tasks during load increases.

[0104] Under the above operating conditions, the output heat power curve, the average pressure observation curve, and the outlet pressure curve of each energy hub are respectively as follows: Figure 8 , 9 As shown in Figure 10.

[0105] It can be seen that the proposed heat network control strategy can not only achieve the proportional distribution of the heat power required by the load among the energy hubs, but also restore the average pressure observation value of each energy hub to the rated value of 1.16 MPa. The heat network reaches stability again after about 75 seconds, and the transient process is much longer than that of the power grid.

[0106] Taking a 150s heat load increase as an example, the control process of the heating network is described as follows: When the heat load increases, the outlet pressure drops. Each energy hub rapidly increases its heat power output according to the droop equation. However, due to the different resistances of the transmission pipes of each EH, the change in outlet pressure is also different, resulting in an error in the heat power output of the droop control. At the same time, the heat power distribution error is distributed and adaptively iterated among each EH through a consensus algorithm to adjust the heat power output value until the proportional distribution requirement is met. In addition, the pressure deviation is adjusted by the pressure deviation controller, which restores the average outlet pressure to the reference value by modifying the local pressure setpoint. As shown in the figure, the larger the heat power capacity of the energy hub, the smaller the change in the average pressure observation value. This is because the power increment output according to the droop control is larger, which to some extent alleviates the drop in its own pressure.

[0107] This invention also provides a distributed collaborative control system for energy hub clusters including energy storage, used to execute the method described above, the system comprising:

[0108] Multiple energy hubs, each of which includes energy storage equipment, energy conversion equipment, local controllers, and communication modules;

[0109] An electric power network, connecting the aforementioned energy hubs, is used to transmit electrical energy;

[0110] A heating network, connecting all the aforementioned energy hubs, is used to transmit heat energy;

[0111] A communication network, connecting the communication modules of each of the energy hubs, for enabling data interaction;

[0112] The local controller for each of the energy hubs includes:

[0113] A primary droop control module is used to initially allocate electrical or thermal power according to the capacity ratio based on local frequency or pressure deviation.

[0114] The secondary power control module, based on the consensus algorithm, utilizes the communication network to exchange state information, introduces frequency recovery error terms and power allocation error terms, and corrects the primary control results to achieve frequency recovery and precise power allocation.

[0115] The heating network average pressure observer module, based on a consensus algorithm, collaboratively calculates the average pressure of the heating network and corrects pressure deviations through a PI controller to ensure the stability of the heating network pressure.

[0116] When electrical and thermal loads change, the system achieves dynamic power allocation and network parameter recovery for each energy hub through the coordinated control of various modules.

[0117] This invention has the following features and effects:

[0118] 1. High reliability and fault tolerance: The system adopts a hierarchical distributed control structure. The primary droop control module can achieve initial power allocation by relying only on local information. Even if the communication network fails, the system can still maintain basic operation. The secondary control is based on a consensus algorithm, which avoids dependence on the central controller and eliminates the risk of single point of failure.

[0119] 2. Precise frequency and power coordinated control: The secondary power control module adopts a leader-led consensus algorithm. By introducing frequency recovery error terms and power distribution error terms, it not only restores the system frequency to the rated value, but also ensures that the output power of each energy hub is strictly and accurately distributed according to the capacity ratio, with fast dynamic response and high steady-state accuracy.

[0120] 3. Stable and adaptive adjustment of heating network pressure: The heating network average pressure observer module calculates the average pressure of the entire network through a consensus algorithm and corrects the pressure deviation by combining it with a PI controller. This solves the problem that traditional droop control cannot eliminate static pressure difference, ensuring that the heating network pressure is stable within the rated range and that the transient process is smooth.

[0121] 4. Multi-energy flow integrated coordination: The system takes into account the strong coupling characteristics of grid frequency and heating network pressure, realizes the joint optimization allocation of electrical and thermal power, and improves the multi-energy complementary operation efficiency and energy supply quality of microgrid.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0127] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A distributed collaborative control method for energy hub clusters including energy storage, characterized in that, Includes the following steps: Step 1: Construct a power network model, which describes the coupling mechanism between the frequency and voltage of the power network and the active and reactive power transmitted through the lines; Step 2: Construct a heating network model, which describes the coupling mechanism between port pressure and pipeline flow in the heating network; Step 3: Based on the power network model and heating network model, design a primary droop control strategy for each energy hub to initially allocate electrical or thermal power according to the local frequency or pressure deviation and the capacity ratio. Step 4: Based on the consensus algorithm, design the secondary control strategy for each energy hub, utilize the communication network to exchange state information, introduce frequency recovery error term and power allocation error term, correct the primary control results, and realize frequency recovery and accurate power allocation; Step 5: Based on the consensus algorithm, design a heating network average pressure observer. Each energy hub collaboratively calculates the heating network average pressure, and a PI controller corrects the pressure deviation to ensure the stability of the heating network pressure. Step 6: When the electrical and thermal loads change, execute steps 3 to 5 in real time to achieve dynamic and coordinated control of each energy hub.

2. The method of claim 1, wherein, The construction of the power network model in step 1 specifically includes: a single power source is connected to the AC bus through an inductive branch, and the output apparent power is: ； where S is apparent power, Vout is the power supply output voltage, Vbus is the common bus voltage, Z is the line impedance; From the above formula, the output active power P and reactive power Q are respectively: ； ； For AC high-voltage power grid, line reactance X is much larger than resistance R, so the phase angle of line voltage is not much different, approximately , , so active frequency and reactive power have strong coupling characteristics, expressed as: ； 。 3. The method of claim 2, wherein, The power network model focuses on the strong coupling characteristics of active power and frequency. The relationship between the frequency and output power at each energy hub's grid connection point can be represented by the characteristics of synchronous machine nodes. ； where δ is the node power angle, ω and ω s are the normalized and reference values of the node angular frequency, T j is the equivalent time constant, P m and P e are the input mechanical power and the output electromagnetic power, respectively, and D represents the damping coefficient.

4. The method of claim 1, wherein, Step 2 involves constructing a heating network model, specifically including: Under flow control mode, the flow rate in the transmission pipes of the heating network and the port pressure have strong coupling characteristics, represented as: ； Among them, S l S is the equivalent resistance of the load pipeline. t =S OP +S IP The total resistance of the transmission pipeline, including the resistance S of the water supply pipeline. OP Return water pipe resistance S IP L hn Let N be the heat output of energy hub n, N be the number of energy hubs, and P be the outlet pressure of the energy hub. in For the inlet pressure of the energy hub, V t This indicates the volumetric flow rate of the transmission pipeline. The temperature loss of the load pipeline is represented by c, which represents the specific heat capacity of the heat transfer medium, and ρ is the density of the heat transfer medium.

5. The method of claim 4, wherein, The pressure difference of each energy hub inlet and outlet is affected by both the thermal load and the output thermal power of each energy hub hn Corresponding to the transmission pipeline flow V t Corresponding to the transmission pipeline flow V Corresponding to the transmission pipeline flow V ； wherein S1-S M represents the resistance of the pipe corresponding to each heat load.

6. The method of claim 1, wherein, In step 3, the control of the electrical output of each energy hub adopts a hierarchical distributed control structure. The primary control adopts a droop-based control logic, as follows: ； ； Among them, L ei and L eNi f represents the actual and rated power output of the i-th energy hub, respectively. i Let f be the measured frequency value of the i-th energy hub, and f be the frequency rating. N = 50Hz, k pi Let be the droop coefficient of the electrical droop control equation for the i-th energy hub.

7. The method of claim 1, wherein, In step 3, the control of the thermal energy output of each energy hub adopts a hierarchical distributed control structure. The primary control uses a consistency-based droop control logic, represented as follows: ； ； Among them, L hi and L hNi S represents the actual and rated output thermal power of the i-th energy hub, respectively. hi and S hj The maximum thermal power output of the i-th and j-th energy hubs, i.e., thermal power capacity, are given by k. qi p is the droop coefficient of the thermal droop control equation for the i-th energy hub. i and p Ni Let u be the measured and rated pressure of the outlet pipeline of the i-th energy hub. hi R represents the power distribution error term. h This is the gain coefficient for power distribution error.

8. The method of claim 1, wherein, In step 4, the secondary control uses a consensus algorithm to introduce a frequency error correction term, expressed as: ； ； Among them, c fi For the frequency recovery error term, c ei S is the power distribution error term. ei For the maximum electrical power output of the i-th energy hub, i.e., the electrical power capacity, g i The gain is the difference between the node's actual frequency and its rated frequency. For the leader node, g i = 1, all follower nodes are 0, a ij Let a be an element of the adjacency matrix of nodes i and j. When there is communication between nodes i and j, a ij =1, otherwise, a ij = 0.

9. The method of claim 1, wherein, In step 5, an average pressure observer for the heating network of the energy hub is constructed based on the consensus algorithm, namely: ； wherein Pout,i is the average value of the observed heat grid outlet pressure for the i-th energy hub; The average pressure observation value is compared with the district heating network pressure reference value The pressure deviation is obtained, and a pressure deviation correction amount is generated by a PI controller As shown in the following formula: ； Among them, G i (s) is the transfer function of the PI controller, u pi This indicates the pressure deviation correction term.

10. A distributed collaborative control system for energy hubs cluster with energy storage, characterized in that, The system for performing the method according to any one of claims 1-9 comprises: Multiple energy hubs, each of which includes energy storage equipment, energy conversion equipment, local controllers, and communication modules; An electric power network, connecting the aforementioned energy hubs, is used to transmit electrical energy; A heating network, connecting all the aforementioned energy hubs, is used to transmit heat energy; A communication network, connecting the communication modules of each of the energy hubs, for enabling data interaction; The local controller for each of the energy hubs includes: A primary droop control module is used to initially allocate electrical or thermal power according to the capacity ratio based on local frequency or pressure deviation. The secondary power control module, based on the consensus algorithm, utilizes the communication network to exchange state information, introduces frequency recovery error terms and power allocation error terms, and corrects the primary control results to achieve frequency recovery and precise power allocation. The heating network average pressure observer module, based on a consensus algorithm, collaboratively calculates the average pressure of the heating network and corrects pressure deviations through a PI controller to ensure the stability of the heating network pressure. When electrical and thermal loads change, the system achieves dynamic power allocation and network parameter recovery for each energy hub through the coordinated control of various modules.

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