Energy storage type energy hub self-organization optimization control method and control system thereof

Abstract

The invention belongs to the technical field related to energy dispatching, and discloses an energy storage type energy hub self-organization optimization control method and a control system thereof.The control method comprises the steps that an energy hub is divided into a power supply model and a heat supply model, and the power cost increment rate of each device in the power supply model serves as a state variable to be decided; taking the thermal cost micro-increase rate of each device in the heat supply model as a to-be-decided state variable, and respectively solving the power supply model and the heat supply model by adopting a leader consistency algorithm under the constraint of power balance to obtain the optimal power cost micro-increase rate of each device in the power supply model and the optimal thermal cost micro-increase rate of each device in the heat supply model; and obtaining the optimal output power of each device based on the optimal cost increment rate and the function of the cost increment rate with respect to the output power. According to the scheme, the communication burden and the calculation cost of energy hub internal equipment optimization control can be effectively reduced, and the method is particularly suitable for a large-scale micro energy network with a complex topological structure.

Description

Technical Field

[0001] This invention belongs to the field of energy dispatching technology, and more specifically, relates to a self-organizing optimization control method and control system for energy storage-type energy hubs. Background Technology

[0002] With the escalating energy crisis and environmental pollution, the sustainability of traditional energy consumption patterns is being challenged. People are beginning to explore the integrated utilization of multiple energy forms, such as electricity, gas, and heat, to achieve a more efficient and environmentally friendly energy supply. Different energy forms are strongly coupled in production, transmission, and consumption. By optimizing the synergistic complementarity of multiple energy flows, energy utilization efficiency can be significantly improved, supply costs reduced, and dependence on a single energy form decreased.

[0003] Microgrids have emerged as an innovative energy management model, not only breaking through the bottlenecks of traditional power systems but also providing greater optimization space for the consumption of renewable energy. Energy hubs, as the basic units of microgrids, contain various energy conversion devices (such as transformers, electric boilers, and combined heat and power units). The coordinated operation of these devices is crucial for the economic operation of microgrids. Currently, centralized control methods are typically used to optimize the internal control of multiple energy hubs. However, this method suffers from heavy communication burdens and high computing costs, making it difficult to adapt to large-scale, complex microgrid topologies.

[0004] Therefore, there is an urgent need to propose a new control method to reduce the communication burden and computing costs of optimizing the control of equipment within energy hubs. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides an energy hub self-organizing optimization control method and its control system, the purpose of which is to reduce the communication burden and computing cost of optimization control of internal equipment in the energy hub.

[0006] To achieve the above objectives, this invention is proposed.

[0007] According to a first aspect of the present invention, a self-organizing optimization control method for an energy hub is provided, comprising:

[0008] The energy hub is divided into a power supply model and a heating model. The power supply model includes all devices in the energy hub that can output electrical energy, and the heating model includes all devices in the energy hub that can output thermal energy. The electrical communication topology between devices in the power supply model and the thermal communication topology between devices in the heating model are determined.

[0009] In the power communication topology, the node that can acquire the output power data of each node in the topology and the external power load data is designated as the leader node of the topology. Similarly, in the thermal communication topology, the node that can acquire the output thermal power data of each node in the topology and the external thermal load data is designated as the leader node. In the power supply model, the incremental rate of electricity cost of each device is the state variable to be decided. In the heating model, the incremental rate of thermal cost of each device is the state variable to be decided. Under the constraint of power balance, a consensus algorithm with a leader is used to solve the power supply model and the heating model respectively, to obtain the optimal incremental rate of electricity cost of each device in the power supply model and the optimal incremental rate of thermal cost of each device in the heating model.

[0010] For a device that outputs a single energy source, the optimal output power is obtained based on its optimal cost increment rate and the function of its cost increment rate with respect to output power. For a combined heat and power (CHP) device, the optimal electrical output power and optimal thermal output power are obtained based on its optimal electricity cost increment rate and optimal thermal cost increment rate, and the function of the corresponding energy cost increment rate with respect to electrical output power and thermal output power.

[0011] According to a second aspect of the present invention, a self-organizing optimization control system for an energy storage hub is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the preceding claims.

[0012] According to a third aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the steps of the method described in any of the preceding claims.

[0013] According to a fourth aspect of the invention, a computer program product is provided, comprising a computer program or instructions, wherein the computer program or instructions, when executed by a processor, implement the steps of the method described in any of the preceding claims.

[0014] In summary, compared with the prior art, the technical solutions conceived in this invention have the following main advantages:

[0015] In this invention, each energy hub in a microgrid optimizes its internal equipment independently, and each energy hub establishes a power supply model and a heating model. Each model selects the incremental cost rate as a state variable and iterates using a leader-led consensus algorithm under power balance constraints. On one hand, the power balance constraint ensures the supply and demand balance of the power grid, guaranteeing system stability. On the other hand, using the incremental cost rate as a state variable and employing consensus iteration to reach cost rate convergence results in a lower total cost for these models, achieving an economically optimal operating condition. Furthermore, since each energy hub is controlled separately and establishes power supply and heating models, the global electro-thermal optimization is transformed into two local optimization problems. Each model uses its own leader node for information coordination and iteration. This optimization solution only requires communication between neighboring devices. Compared to integrated control, this scheme effectively reduces the communication burden and computational cost of optimizing and controlling the internal equipment of the energy hub, making it particularly suitable for large-scale, complex microgrid topologies. Attached Figure Description

[0016] Figure 1 This is a flowchart of the steps of the self-organizing optimization control method for energy storage hubs in one embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of a microgrid topology containing electrical and thermal multi-energy coupling devices in one embodiment;

[0018] Figure 3 This is a communication topology diagram of the devices in an energy hub in one embodiment;

[0019] Figure 4 This is a schematic diagram illustrating the principle of equal incremental rate implemented through a consensus iterative algorithm in one embodiment;

[0020] Figure 5 This is a curve showing the change in the device output power of energy hub 1 in one embodiment;

[0021] Figure 6 This is a curve showing the change in the device output power of energy hub 2 in one embodiment;

[0022] Figure 7 This is a curve showing the change in the device output power of energy hub 3 in one embodiment;

[0023] Figure 8 This is a SOC variation curve of an energy storage station in one embodiment. Detailed Implementation

[0024] 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.

[0025] Example 1

[0026] This invention proposes a self-organizing optimization control method for energy storage-type energy hubs, such as... Figure 1 The diagram shown is a flowchart of the self-organizing optimization control method for an energy storage hub according to an embodiment of the present invention. The following is a detailed description of each step.

[0027] S1. Divide the energy hub into a power supply model and a heating model. The power supply model includes all devices in the energy hub that can output electrical energy, and the heating model includes all devices in the energy hub that can output thermal energy. Determine the electrical communication topology between devices in the power supply model and the thermal communication topology between devices in the heating model.

[0028] like Figure 2 The diagram shows a microgrid topology containing multi-energy coupling devices (electric and thermal) in one embodiment. The microgrid topology has several energy hubs, each containing several power-producing devices, typically generating both heat and electricity. The heat is supplied to external heat loads, and the electricity is supplied to external electrical loads. Energy optimization scheduling is performed to achieve supply and demand balance. Typically, energy hubs include both devices that output a single energy source (DOE) and combined heat and power (DTE) devices that output both electricity and heat. For example... Figure 2 In a single energy hub, equipment that outputs a single energy source includes transformers that output only electricity and electric boilers and gas-fired boilers that output only heat. Combined heat and power (CHP) equipment includes CHP units that can output both electricity and heat. Unlike the traditional centralized control of all energy hubs, this invention employs distributed control of energy hubs, where each energy hub optimizes its internal equipment, effectively reducing communication burden and computing costs.

[0029] Furthermore, to further reduce communication and computational costs, this invention establishes and solves separate power supply and heating models for each individual energy hub. The power supply model includes all devices in the energy hub capable of outputting electrical energy, and the heating model includes all devices in the energy hub capable of outputting thermal energy. Figure 2 For example, the power supply model includes transformers and cogeneration units, while the heating model includes electric boilers, gas boilers, and cogeneration units.

[0030] The electrical communication topology between devices in the power supply model and the thermal communication topology between devices in the heating model are determined separately. Specifically, in each model, the directed graph G = (V G E G A G Describe its communication topology, where the node set consisting of all nodes is represented by V. G ={1,2,...,N) means that each device in the model can be viewed as a node in a directed graph. Let E be the set of edges consisting of all connected nodes, where (i,j)∈E G This indicates that there is a connection between node i and node j, meaning that i and j are neighboring nodes. The devices they represent can exchange variable information through communication. In this invention, the variable information of a device is its output power information.

[0031] More specifically, the communication topology of the corresponding model can be represented using an adjacency matrix. The element a in the i-th row and j-th column of the adjacency matrix... ij This represents the communication weight between the i-th device and the j-th device. If there is a communication relationship between them, the communication weight is 1; otherwise, it is 0. For example, if there are three devices in the model, a 3x3 adjacency matrix can be constructed as follows:

[0032]

[0033] In the above methods, based on graph theory, a communication topology between multi-energy coupled devices is constructed through a directed graph. This communication topology can intuitively reflect the interactive communication logic between devices, providing a communication foundation for subsequent optimization control.

[0034] S2. In the power communication topology, the node that can obtain the output power data of each node in the communication topology and the external power load data is taken as the leader node of the communication topology. Similarly, in the thermal communication topology, the node that can obtain the output thermal power data of each node in the communication topology and the external thermal load data is taken as the leader node of the communication topology. The incremental rate of electricity cost of each device in the power supply model and the incremental rate of thermal cost of each device in the heating model are taken as state variables to be decided. Under the constraint of power balance, the consensus algorithm is used to solve the power supply model and the heating model respectively to obtain the optimal incremental rate of electricity cost of each device in the power supply model and the optimal incremental rate of thermal cost of each device in the heating model.

[0035] Specifically, after obtaining the communication topology of each model, the leader node in each model is first determined. This leader node is capable of acquiring the output power information of other nodes in the model, as well as the external load power information. In the power supply model, the leader node is the device capable of acquiring the output electrical power data of each device in the model and the external electrical load data. In the heating model, the leader node is the device capable of acquiring the output thermal power data of each device in the model and the external thermal load data.

[0036] Figure 3 This is a communication topology diagram of the devices in an energy hub in one embodiment, to... Figure 3 For example, a transformer can obtain information on sudden changes in external electrical load and the electrical output power information of each internal electrical energy output device. Therefore, in the power supply model, the transformer is used as the leader node. An electric boiler can obtain information on sudden changes in external heat load and the heat output power information of each internal heat energy output device. Therefore, in the heating model, the electric boiler is used as the leader node.

[0037] After determining the leader node in the heating model, the incremental rate of heat cost for each device in the model is used as the state variable to be decided. Under the constraint of heat power balance, a consensus algorithm is used to solve the heating model to obtain the optimal incremental rate of heat cost for each device in the heating model. It can be understood that the constraint of heat power balance is the balance between heat power supply and demand.

[0038] After determining the leader node in the power supply model, the incremental rate of electricity cost for each device in the model is used as the state variable to be decided. Under the constraint of power balance, a consensus algorithm is used to solve the power supply model to obtain the optimal incremental rate of electricity cost for each device in the model. It can be understood that the constraint of power balance is the balance between power supply and demand.

[0039] Among them, the incremental rate of thermal cost is the rate of change of equipment cost with its thermal output power, and the incremental rate of electrical cost is the rate of change of equipment cost with its electrical output power.

[0040] In this invention, a leader is selected in the model, and the supply and demand deviation information of the model can be transmitted to each device in the model through the leader, thereby realizing the tracking of the target value.

[0041] In this invention, by selecting the cost increment rate as the state variable for consistent iteration, the cost increment rate of each device can be converged through a distributed method. The converged cost increment rate can make the total cost of these models smaller, that is, achieve a more economical operating condition.

[0042] Specifically, a leader-led discrete consensus algorithm can be used to achieve self-organizing optimization control, wherein:

[0043] Iterative formula for leaders:

[0044]

[0045] Iterative formula for non-leaders:

[0046]

[0047] In the formula, λ zi (k) represents the incremental cost rate obtained by the k-th iteration of the i-th device in the model, z represents the energy type; when it is a power supply model, z represents electrical energy, and when it is a heating model, z represents thermal energy. ij This represents the communication weight between the i-th and j-th devices in the model. If there is a communication relationship between them, the communication weight is 1; otherwise, it is 0. N is the number of devices in the model, ε is the preset power deviation correction coefficient, and ΔP z =∑P outi -P load P represents the power difference between supply and demand. outi Let ∑P be the output power of the i-th device corresponding to the z-th energy source in the model. outi P represents the total output power of all devices in the model corresponding to the z-th energy source. load Let z be the external power load of the z-th energy source.

[0048] In the leader-led consensus iterative formula used above, a power imbalance term ΔP is introduced. z =∑P outi -P load This allows the leader node to respond to network load changes and dynamically adjust the output of each device to meet real-time power balance requirements when there are sudden changes in load power.

[0049] The leader-led consensus iterative approach used above employs a discrete consensus algorithm. In practice, continuous consensus algorithms have high communication requirements, which are often difficult to achieve. The discrete consensus algorithm used in the above method is more in line with engineering practice. The discrete consensus algorithm writes the differential of variables in difference form. Selecting an appropriate sampling step size can usually meet the consensus convergence requirements, and the communication burden is also greatly reduced.

[0050] Based on the above steps, for equipment that only outputs electrical energy, the optimal incremental rate of electricity cost is obtained by solving the power supply model; for equipment that only outputs heat energy, the optimal incremental rate of heat cost is obtained by solving the heat supply model; and for combined heat and power (CHP) equipment, the optimal incremental rate of electricity cost and the optimal incremental rate of heat cost are obtained by solving the power supply model and the heat supply model, respectively.

[0051] S3. For a device that outputs a single energy source, the optimal output power of the device is obtained based on its optimal cost increment rate and the function of its cost increment rate with respect to the output power. For a combined heat and power (CHP) device, the optimal electrical output power and optimal thermal output power of the device are obtained based on its optimal electricity cost increment rate and optimal thermal cost increment rate, and the function of its corresponding energy cost increment rate with respect to the electrical output power and thermal output power.

[0052] In this invention, the cost of each device is constructed using the output power of the device as a variable. Therefore, the incremental cost rate of the device is also a function related to the output power. After obtaining the optimal incremental cost rate of each device, the corresponding optimal output power can be solved based on the function of device cost with respect to output power.

[0053] Specifically, equipment costs can be constructed as a quadratic function.

[0054] For a device that outputs a single energy source, its output power is either electrical or thermal. Therefore, its equipment cost only includes one output power variable, which can be expressed in the following form:

[0055] C w (L w )=Pr w (α w L 2 w +β w L w +γ w );

[0056] In the formula, L w For the output power of a device w that outputs a single energy source, C w (L w ) indicates that the output power of device w is L w The cost of its input energy, Pr w Let α be the unit price of the input energy for device w. w β w γ w Let be the coefficients of each term in the cost function of device w.

[0057] For combined heat and power (CHP) equipment, since its output power includes both electrical and thermal output power, its equipment cost includes two output power variables, which can be expressed in the following form:

[0058] C v (L e,v ,L h,v )=Pr v (α v L 2 e,v +β v Le,v +τ v L 2 h,v +ε v L h,v +μ v L e,v L h,v +γ v );

[0059] In the formula, L e,v L h,v These represent the electrical output power and thermal output power of the combined heat and power (CHP) unit, respectively. v (L e,v ,L h,v () indicates that the output power of device v is L e,v The output thermal power is L h,v The cost of its input energy, Pr v Let α be the unit price of the input energy for device v. v β v γ v τ v ε v μ v These are the coefficients of each term in the cost function of device v.

[0060] The coefficients of each of the above terms can be determined through previous experiments.

[0061] Understandably, based on the cost functions of the above devices, by taking their derivatives, we can obtain the incremental cost rate as a function of the corresponding energy output power. For example, differentiating the cost function with respect to electrical output power yields an expression for the incremental cost rate of electricity, and differentiating the cost function with respect to thermal output power yields an expression for the incremental cost rate of thermal power.

[0062] For example, the incremental cost λ of a device w that outputs a single energy source. w It can be represented as:

[0063]

[0064] By substituting the optimal cost increment rate of a device that outputs a single energy source into the above expression, the optimal output power can be calculated.

[0065] The incremental rate of electricity cost λ for combined heat and power (CHP) equipment v e,v It can be represented as:

[0066]

[0067] The incremental rate of heat cost λ for cogeneration equipment v h,v It can be represented as:

[0068]

[0069] Substituting the optimal incremental rates of electricity and heat costs for the combined heat and power (CHP) equipment into the two expressions above, and solving them simultaneously, the optimal electrical output power and optimal heat output power can be calculated.

[0070] In one embodiment, by further modifying the above cost increment rate, the expression for output power with respect to the cost increment rate is derived in reverse. In actual operation, the cost increment rate can be directly substituted into the expression for output power to directly calculate the corresponding output power.

[0071] The expression for the optimal output power of device w, which outputs a single energy source, is:

[0072]

[0073] The expressions for the optimal thermal output power and optimal electrical output power of the combined heat and power (CHP) unit v are:

[0074]

[0075] In the formula, To determine the optimal output power of device w that outputs a single energy source, These represent the optimal thermal output power and optimal electrical output power of the combined heat and power (CHP) unit v, respectively. These are the optimal incremental cost rates for device w, and the optimal incremental thermal cost rates and optimal incremental electrical cost rates for device v, respectively, obtained from the model solution.

[0076] In one embodiment, considering that the optimal power of the device obtained based on the consensus algorithm cannot be guaranteed to be within the upper and lower limits of the actual power of the device, it is necessary to impose upper and lower limit constraints on the optimal power value of the device derived from the incremental cost rate:

[0077] If the obtained optimal energy output power is within the safe range of the corresponding device's output power, then the optimal energy output power is output.

[0078] If the obtained optimal energy output power exceeds the corresponding device's output power safety range, then the upper limit of the output power safety range shall be taken as the optimal energy output power.

[0079] If the optimal energy output power obtained based on the optimal cost incremental rate is lower than the corresponding device's output power safety range, then the lower limit of the output power safety range shall be taken as the optimal energy output power.

[0080] Specifically, it can be represented in the following form:

[0081]

[0082] Where z represents the type of energy. and These are the upper and lower limits of the energy output z of device r, respectively. z,r Let z be the actual power output of the energy source z by device r.

[0083] Based on the above steps, the optimal output power of each device in the energy hub can be obtained, realizing self-organizing optimization control within the energy hub.

[0084] Overall, since each energy hub is controlled separately and power supply and heating models are established to convert the global electrothermal optimization into two local optimization problems, and each model uses its own leader node for information coordination and iteration, the optimization solution only requires communication between neighboring devices. Compared with integrated control, this scheme can effectively reduce the communication burden and computational cost of device optimization control within the energy hub, and is especially suitable for large-scale, complex topology microgrids.

[0085] Example 2

[0086] The present invention also relates to a self-organizing optimization control system for an energy storage type energy hub. The system can be installed on an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0087] The electronic device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor performs various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0088] Example 3

[0089] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0090] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0091] Example 4

[0092] This invention provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the method described in the above embodiments of this invention.

[0093] Example 5

[0094] by Figure 2 and Figure 3 The microgrid system with multiple energy flows including electricity and heat shown is an example to verify the effectiveness of the proposed method. This microgrid has a typical topology containing three energy hubs (EHs). Each EH includes basic energy conversion equipment to achieve secondary distribution of input electrical energy and natural gas from the energy supply side to the energy output side, including electricity and heat. The electrical and thermal power capacities of each energy hub are shown in Tables 1 and 2. The simulation settings are as follows: initial electrical load and thermal load are 3500kW and 1750kW respectively; after 150s, the electrical load increases by 1050kW and the thermal load increases by 630kW; after 300s, the electrical load decreases by 1750kW and the thermal load decreases by 1050kW. The numerical simulation was performed in MATLAB.

[0095] Table 1 DOE Equipment Parameters

[0096]

[0097] Table 2 DTE Unit Parameters

[0098]

[0099] Figure 4 This diagram illustrates the principle of equal incremental rate achieved through a consensus iterative algorithm. The result of the consensus iterative algorithm is that the incremental rate of cost for each device in the model converges, exhibiting an equal incremental rate trend.

[0100] Figure 5 , Figure 6 , Figure 7The output power variation curves of equipment EH1, EH2, and EH3 are shown respectively. In the legend, e and h represent the output electrical and thermal power, respectively.

[0101] In this embodiment, the leader node for electrical power balance is the transformer, and the leader node for thermal power balance is the gas-fired boiler. Through the energy hub self-organizing optimization control method proposed in this invention, distributed self-consistent control of the energy hub is achieved. The inputs, outputs, and economic costs of each energy hub are shown in Table 3. For comparison, traditional centralized optimization control of energy hubs is used, and the inputs, outputs, and economic costs of each energy hub are shown in Table 4. In the table, operating conditions 1, 2, and 3 correspond to the initial time, 150s-300s, and 300s later, respectively, representing the three periods of steady-state EH operation.

[0102] Table 3 Energy consumption and economic costs of distributed autonomous control

[0103]

[0104] Table 4 shows the energy consumption and economic costs of centralized optimization solutions.

[0105]

[0106] The following conclusions can be drawn from the experiment:

[0107] 1) The self-organizing optimization control method for energy hubs with energy storage proposed in this invention has an economic cost close to that obtained by centralized optimization, and in some cases, the cost is slightly higher than that obtained by centralized optimization. The reason for this is that the self-organizing optimization control method for energy hubs proposed in this invention transforms the global optimization problem into two local optimization problems. The result may not be the cost-optimal solution. However, this method only requires communication between neighboring devices and optimizes the output power through a consensus algorithm. Compared with integrated control, this scheme can effectively reduce the communication burden and computational cost of optimization control of devices within the energy hub, and is especially suitable for microgrids with large-scale and complex topologies.

[0108] 2) Regardless of whether it is the distributed optimization of this invention or the traditional centralized optimization solution, the economic cost per unit power of EH increases with the increase of output power, which is consistent with the change of cost increment rate during distributed economic scheduling.

[0109] 3) The electrical and thermal power outputs of DTE units are strongly coupled, mutually constraining each other during distributed economic dispatch. On the one hand, the output electrical and thermal power can only be obtained after the incremental rates of electrical and thermal power are determined; on the other hand, a large output of one type of power will constrain the output of another type of power. For example, in EH1, the DTE unit always outputs electrical power at full load, and the thermal power is only used to supplement the shortage of other equipment; in EH3, the DTE unit always outputs thermal power at full load, while the electrical power is always zero.

[0110] Each energy hub obtains input energy through large-scale centralized energy storage power stations and gas storage stations. The energy storage equipment can be uniformly modeled as follows:

[0111]

[0112] in, Let i be the energy of energy storage station i at time t+1. The charging / discharging efficiency is given by Δt, which represents the time interval. Based on the equipment's output power, its consumed electrical energy and fuel gas (U) can be calculated using the following formula:

[0113] U w (L w )=α w L 2 w +β w L w +γ w

[0114] U v (L e,v ,L h,v )=α v L 2 e,v +β v L e,v +τ v L 2 h,v +ε v L h,v +λ v L e,v L h,v +γ v

[0115] Based on the energy storage device model, the state of energy (SOC) changes of the energy storage station during the operation of a single energy hub are as follows: Figure 8 As shown, the energy hub can adjust energy use in a timely manner. For example, when the electrical storage is running low, more natural gas is used subsequently, achieving flexible adjustment of energy utilization.

[0116] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "again" are intended to illustrate the present invention and are not intended to limit the present invention.

[0117] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A self-organizing optimization control method for energy storage-type energy hubs, characterized in that, include: The energy storage hub is divided into a power supply model and a heating model. The power supply model includes all devices in the energy hub that can output electrical energy, and the heating model includes all devices in the energy hub that can output thermal energy. The electrical communication topology between devices in the power supply model and the thermal communication topology between devices in the heating model are determined. The input energy of the energy storage hub is provided by an energy storage power station. In the power communication topology, the node that can acquire the output power data of each node in the topology and the external power load data is designated as the leader node of the topology. Similarly, in the thermal communication topology, the node that can acquire the output thermal power data of each node in the topology and the external thermal load data is designated as the leader node. In the power supply model, the incremental rate of electricity cost of each device is the state variable to be decided. In the heating model, the incremental rate of thermal cost of each device is the state variable to be decided. Under the constraint of power balance, a consensus algorithm with a leader is used to solve the power supply model and the heating model respectively, to obtain the optimal incremental rate of electricity cost of each device in the power supply model and the optimal incremental rate of thermal cost of each device in the heating model. For a device that outputs a single energy source, the optimal output power is obtained based on its optimal cost increment rate and the function of its cost increment rate with respect to output power. For a combined heat and power (CHP) device, the optimal electrical output power and optimal thermal output power are obtained based on its optimal electricity cost increment rate and optimal thermal cost increment rate, and the function of the corresponding energy cost increment rate with respect to electrical output power and thermal output power.

2. The control method as described in claim 1, characterized in that, The process of solving the power supply model and the heating model using a consensus algorithm under the constraint of power balance includes iteratively solving each model using the following consensus algorithm iterative formula: The iterative formula for the leader node: Iterative formula for non-leader nodes: In the formula, λ zi (k) represents the incremental cost rate obtained by the k-th iteration of the i-th device in the model, z represents the energy type; when it is a power supply model, z represents electrical energy, and when it is a heating model, z represents thermal energy. ij This represents the communication weight between the i-th and j-th devices in the model. If there is a communication relationship between them, the communication weight is 1; otherwise, it is 0. N is the number of devices in the model, ε is the preset power deviation correction coefficient, and ΔP z =∑P outi -P load P represents the power difference between supply and demand. outi Let ∑P be the output power of the i-th device corresponding to the z-th energy source in the model. outi P represents the total output power of all devices in the model corresponding to the z-th energy source. load Let z be the external power load of the z-th energy source.

3. The control method as described in claim 1, characterized in that, Both the electrical energy communication topology and the thermal energy communication topology are represented in the form of adjacency matrices, where the element a in the i-th row and j-th column of the adjacency matrix... ij This represents the communication weight between the i-th device and the j-th device. If there is a communication relationship between the two devices, the communication weight is 1; otherwise, it is 0.

4. The control method as described in claim 1, characterized in that, The incremental cost rate of a single-energy-output device, as a function of the corresponding energy output power, is obtained by differentiating the cost function of the device with respect to the corresponding energy output power. Similarly, the incremental cost rate of a combined heat and power (CHP) device, as a function of both electrical and thermal output power, is obtained by differentiating the cost function of the device with respect to the corresponding energy output power. The cost function of a device that outputs a single energy source is: C w (L w )=Pr w (α w L 2 w +β w L w +γ w , In the formula, L w For the output power of a device w that outputs a single energy source, C w (L w ) indicates that the output power of device w is L w The cost of its input energy, Pr w Let α be the unit price of the input energy for device w. w β w γ w Let be the coefficients of each term in the cost function of equipment w; The cost function of a combined heat and power (CHP) system is: C v (L e,v ,L h,v )=Pr v (α v L 2 e,v +β v L e,v +τ v L 2 h,v +ε v L h,v +μ v L e,v L h,v +γ v , In the formula, L e,v L h,v These represent the electrical output power and thermal output power of the combined heat and power (CHP) unit, respectively. v (L e,v ,L h,v () indicates that the output power of device v is L e,v The output thermal power is L h,v The cost of its input energy, Pr v Let α be the unit price of the input energy for device v. v β v γ v τ v ε v μ v These are the coefficients of each term in the cost function of device v.

5. The control method as described in claim 4, characterized in that, Substituting the obtained optimal incremental rates of electricity cost and optimal incremental rates of heat cost into the expression for the optimal output power of the corresponding equipment, we obtain the optimal output power of the corresponding equipment, where; The expressions for the optimal output power of a device that outputs a single energy source, and the expressions for the optimal electrical output power and optimal thermal output power of a combined heat and power (CHP) device, are as follows: In the formula, To determine the optimal output power of device w that outputs a single energy source, These represent the optimal thermal output power and optimal electrical output power of the combined heat and power (CHP) unit v, respectively. These are the optimal incremental cost rates for device w, and the optimal incremental thermal cost rates and optimal incremental electrical cost rates for device v, respectively, obtained from the model solution.

6. The control method as described in claim 1, characterized in that, The energy hub includes transformers and combined heat and power (CHP) units that can output electrical energy, and electric boilers, gas-fired boilers, and CHP units that can output thermal energy. Transformers, electric boilers, and gas-fired boilers are all devices that output a single energy source, while CHP units are combined heat and power devices that output both electrical energy and thermal energy.

7. The control method as described in claim 1, characterized in that, After determining the optimal energy output power based on the optimal cost incremental rate, the following judgment is also made: If the obtained optimal energy output power is within the safe range of the corresponding device's output power, then the optimal energy output power is output. If the obtained optimal energy output power exceeds the corresponding device's output power safety range, then the upper limit of the output power safety range shall be taken as the optimal energy output power. If the optimal energy output power obtained based on the optimal cost incremental rate is lower than the corresponding device's output power safety range, then the lower limit of the output power safety range shall be taken as the optimal energy output power.

8. A self-organizing optimization control system for an energy storage-type energy hub, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 7.

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