Robust optimization method and system for multi-energy microgrid considering load importance ranking

Abstract

The present application relates to a kind of multi-energy microgrid robust optimization method and system considering load importance grading, the method includes: the load model of multi-energy microgrid system is established;Electric load and heat load are classified according to importance, the outage sequence of different importance load is controlled by binary variable, and electric, heat load importance classification outage model under energy supply interruption is established;Considering the uncertainty of photovoltaic output, external energy network interruption time, with the minimum load reduction loss of multi-energy microgrid as objective function, a robust elastic scheduling optimization model considering multi-energy load sequence reduction is constructed.The present application establishes the sequential outage model of different importance level load, constructs the robust optimization scheduling model of multi-energy microgrid considering load grading facing elasticity with the minimum load outage level as target, so as to achieve the sequence reduction effect under energy supply interruption, reduce the load outage loss of multi-energy microgrid, and improve the elastic ability of system.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy microgrid optimization scheduling, and in particular to a multi-energy microgrid robust optimization method and system considering load importance grading. Background Art

[0002] Multi-energy microgrids are energy systems that incorporate distributed generation units, energy storage devices, and flexible loads. They offer significant advantages in efficient energy utilization and renewable energy integration, playing a crucial role in achieving my country's "dual carbon" strategic goals. However, the complexity of multi-energy coupling complicates the analysis and improvement of system resilience. For example, the cross-system propagation of faults caused by multi-energy coupling poses new challenges to the operation of multi-energy microgrids during disasters.

[0003] To date, research has yielded valuable insights into optimizing the resilient operation of multi-energy systems. Power outages are common and unavoidable in various resilience scenarios. However, the loss of loads of varying importance can lead to varying losses in microgrids.

[0004] Currently, existing multi-energy microgrid scheduling and operation schemes fail to fully consider the hierarchical management of power loads. Specifically, when a system failure occurs or enters island operation mode, there is no clear load importance classification or sequential load reduction strategy. This results in the system shedding load randomly or in a fixed sequence in an emergency, failing to minimize the disruption of critical loads and increasing system outage losses. Summary of the Invention

[0005] In view of this, it is necessary to provide a multi-energy microgrid robust optimization method that takes load importance grading into consideration to solve the above-mentioned defects of the prior art.

[0006] To solve the above problems, in a first aspect, an embodiment of the present invention provides a multi-energy microgrid robust optimization method considering load importance classification, comprising:

[0007] Step 1: Establish a load model for a multi-energy microgrid system; wherein the multi-energy microgrid system includes a photovoltaic generator set, a CHP unit, a gas boiler unit, an electric energy storage unit, and a thermal energy storage unit;

[0008] Step 2: Classify the electrical and thermal loads by importance, use binary variables to control the order of removing loads of different importance, and establish a hierarchical removal model for electrical and thermal loads under power outages.

[0009] Step 3: Based on the load model of the multi-energy microgrid system and the hierarchical removal model of the importance of electric and thermal loads under energy supply interruption, a multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function.

[0010] Preferably, in step 1, establishing a load model of the multi-energy microgrid system includes:

[0011] The photovoltaic generator set of the multi-energy microgrid system is modeled as:

[0012]

[0013] Where, represents the output power of the photovoltaic panel at time t, f pv is the derating factor, P rpv is the rated installed capacity of the photovoltaic panels, Indicates the actual light intensity at time t, I STC is the light intensity under standard test conditions, v t is the wind speed, is the surface temperature of the photovoltaic panel, T STC is the standard test temperature, is the ambient temperature;

[0014] The electrical output power and waste heat output power of the CHP unit are expressed by the following formula:

[0015]

[0016] The operating constraints of the CHP unit are expressed as follows:

[0017]

[0018]

[0019] Where, are the electrical power and thermal power output of the CHP unit at time t, is the natural gas power consumed at time t, and are the power generation efficiency and heating efficiency of the CHP unit at time t, and BM is the large M coefficient. is the electric power parameter of the CHP unit, is the thermal power parameter of the CHP unit;

[0020] Gas boiler units are used to convert natural gas into thermal energy and are responsible for peak load regulation. The thermal output power expression of gas boiler units is as follows:

[0021]

[0022] Where, is the heat output power of the gas boiler at time t, is the natural gas power input to the boiler unit at time t, COP boThe energy efficiency coefficient of converting natural gas into thermal energy; is the maximum heat output power of the gas boiler;

[0023] The modeling formula for the electric energy storage unit and the thermal energy storage unit in the multi-energy microgrid system is:

[0024]

[0025] Where, is the stored energy of the storage element at time t, are the initial, maximum and minimum stored energies, Indicates the self-discharge rate of battery energy storage, and is the charging and discharging power of the battery energy storage, Charging power for the battery, is the battery discharge efficiency; △t is the scheduling time interval, and the following constraints are added to limit the operation of the energy storage element:

[0026]

[0027]

[0028] Preferably, in step 2, the electric load and the thermal load are graded according to their importance, and the order of removing loads of different importance is controlled by binary variables to establish an importance graded removal model of electric and thermal loads under energy supply interruption, including:

[0029] The electrical load and thermal load are divided into three levels according to their importance: primary load, secondary load, and tertiary load. Each level of load includes both electrical load and thermal load. Based on the load shedding mechanism of the multi-energy microgrid, a general load formula is established:

[0030]

[0031] Where Y is an auxiliary variable, Y = P represents the power grid, and Y = H represents the heating network; is the initial power at time t, is the amount of power removed at time t; is the load power at time t; Express the power of primary load, secondary load and tertiary load respectively; They represent the amount of removal for different levels of load;

[0032] definition is a binary variable, representing the removal status of the first-level load, the second-level load, and the third-level load, respectively. Different values ​​of the binary variable represent different scenarios of load shedding;

[0033] In order to meet the sequential removal of loads of different importance, the following constraints are established:

[0034]

[0035] Where, are the maximum values ​​of the corresponding loads respectively;

[0036] The load general formula and the constraint conditions constitute a hierarchical removal model of the importance of electrical and thermal loads under energy supply interruption.

[0037] Preferably, in step 3, the multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function, including:

[0038] Considering the uncertainty of energy interruption time and photovoltaic output, the corresponding uncertainty set is established, and the constraints of the multi-energy microgrid robust optimization model are set. The multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function.

[0039] Preferably, the objective function of the multi-energy microgrid robust optimization model is:

[0040]

[0041] Where S is the total load reduction loss of the multi-energy microgrid during the power outage, A v 、B v are the loss weight parameters of electrical and thermal loads respectively.

[0042] Preferably, the operating power constraints of the multi-energy microgrid robust optimization model include:

[0043]

[0044] in, is the input power of the grid at time t, is the maximum input power of the grid at time t. is the input power of the heating network at time t, is the maximum input thermal power of the heating network at time t; are all continuous variables in robust modeling. is the uncertainty parameter related to photovoltaic output, represents the output power of the photovoltaic panel at time t, is the heat release power of thermal energy storage, It is the charging power of thermal energy storage.

[0045] Preferably, the photovoltaic output uncertainty parameter satisfies the following uncertainty modeling formula:

[0046]

[0047] In the formula, is the expected value of photovoltaic active power.

[0048] Considering the uncertainty of energy supply interruption time, the following uncertainty set is established:

[0049]

[0050] wherein, is the uncertainty set of energy supply interruption time, and is an integer variable, representing an additional time period before and after the expected energy supply interruption start and end time, respectively; and are the maximum values of and

[0051] In a second aspect, an embodiment of the present application provides a multi-energy microgrid robust optimization system considering load importance classification, comprising:

[0052] a microgrid modeling module, configured to establish a load model of a multi-energy microgrid system; wherein the multi-energy microgrid system comprises a photovoltaic generator set, a CHP unit, a gas boiler unit, an electric energy storage unit and a thermal energy storage unit;

[0053] a load importance classification module, configured to classify electrical and thermal loads according to importance, control the outage sequence of different importance loads through binary variables, and establish an electrical and thermal load importance classification outage model under energy supply interruption;

[0054] a microgrid optimization module, configured to construct a multi-energy microgrid robust optimization model based on the load model of the multi-energy microgrid system and the electrical and thermal load importance classification outage model under energy supply interruption, with the minimum load reduction of the multi-energy microgrid system as an objective function.

[0055] In a third aspect, the present application further provides an electronic device comprising a memory and a processor, wherein,

[0056] the memory is configured to store a program;

[0057] the processor is coupled to the memory and configured to execute the program stored in the memory to implement the steps in the multi-energy microgrid robust optimization method considering load importance classification according to the first aspect of the present application.

[0058] ​In a fourth aspect, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the multi-energy microgrid robust optimization method considering load importance classification as described in the embodiment of the first aspect of the present invention.

[0059] The robust optimization method and system for a multi-energy microgrid that takes into account load importance grading provided by the present invention not only considers the interaction of various energies between multi-energy microgrids, including electrical energy and thermal energy, but also considers the uncertainty of renewable energy and energy supply interruption time, which is more in line with the actual operation of multi-energy microgrids. The present invention establishes a sequential shedding model for loads of different importance levels, and constructs a multi-energy microgrid robust optimization scheduling model that takes load grading into account and is elastic, by taking the minimum load shedding level as the goal, thereby achieving a sequential load shedding effect in the event of an energy supply interruption, reducing the load shedding loss of the multi-energy microgrid, and improving the elasticity of the system. At the same time, it provides a reference for the design of the proportion of loads of different importance in future multi-energy microgrid systems and the load shedding system under extreme conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flowchart of the robust optimization method for a multi-energy microgrid considering load importance classification provided by the present invention;

[0061] Figure 2 A diagram of the multi-energy microgrid system architecture that takes load importance grading into consideration, provided by the present invention;

[0062] Figure 3 The load curve of the multi-energy microgrid system equipment provided by the present invention;

[0063] Figure 4 A structural block diagram of a multi-energy microgrid robust optimization system considering load importance classification provided by the present invention;

[0064] Figure 5 This is a structural block diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0065] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0066] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] Currently, existing multi-energy microgrid scheduling and operation schemes fail to fully consider the hierarchical management of power loads. Specifically, when a system failure occurs or enters island operation mode, there is no clear load importance classification or sequential load reduction strategy. This results in the system shedding load randomly or in a fixed sequence in an emergency, failing to minimize the disruption of critical loads and increasing system outage losses.

[0068] In light of this, the present invention provides a robust optimization method for a multi-energy microgrid that considers load importance grading. By establishing a sequential shedding model for loads of varying importance, and aiming to minimize load shedding, this robust optimization scheduling model for a multi-energy microgrid that is flexible and considers load grading is constructed. This method achieves sequential load shedding in the event of power outages, reduces load shedding losses in the multi-energy microgrid, and enhances system resilience. This method will be described and illustrated below through multiple embodiments.

[0069] Figure 1 The flowchart of the robust optimization method of multi-energy microgrid considering load importance classification provided by the present invention. Figure 1 As shown in FIG, the robust optimization method of a multi-energy microgrid considering load importance classification includes steps 1 to 3, wherein:

[0070] Step 1: Establish a load model for the multi-energy microgrid system.

[0071] Figure 2 This is a diagram of the multi-energy microgrid system architecture that takes into account load importance classification provided by the present invention. Figure 2 The multi-energy microgrid system includes a photovoltaic power generation unit, a CHP unit, a gas boiler unit, an electric energy storage unit and a thermal energy storage unit.

[0072] Specifically, in addition to purchasing electricity from the grid, a multi-energy microgrid also has photovoltaic generators participating in power generation. The photovoltaic generators in a multi-energy microgrid system are modeled as follows:

[0073]

[0074] Where, represents the output power of the photovoltaic panel at time t, f pv is the derating factor, P rpv is the rated installed capacity of the photovoltaic panels, Indicates the actual light intensity at time t, I STC is the light intensity under standard test conditions, v t is the wind speed, is the surface temperature of the photovoltaic panel, T STC is the standard test temperature, is the ambient temperature;

[0075] During the gas-fired power generation process, a CHP (Combined Heat and Power) unit can utilize a waste heat recovery device to supply heat to the outside. The electrical output power and waste heat output power of the CHP unit are expressed by the following formula:

[0076]

[0077] According to the definition of the typical feasible operating area of ​​CHP, the operating constraints of the CHP unit are expressed as follows:

[0078]

[0079] Where, are the electrical power and thermal power output of the CHP unit at time t, is the natural gas power consumed at time t, and are the power generation efficiency and heating efficiency of the CHP unit at time t, and BM is the large M coefficient. is the electric power parameter of the CHP unit, is the thermal power parameter of the CHP unit.

[0080] Gas-fired boiler units are used to convert natural gas into thermal energy, responsible for peak load regulation, thereby enhancing the flexibility of multi-energy micro-grids. The thermal output power of gas-fired boiler units is expressed as follows:

[0081]

[0082] Where, is the heat output power of the gas boiler at time t, is the natural gas power input to the boiler unit at time t, COP bo The energy efficiency coefficient of converting natural gas into thermal energy; is the maximum heat output power of the gas boiler;

[0083] The modeling formula for the electric energy storage unit and the thermal energy storage unit in the multi-energy microgrid system is:

[0084]

[0085] Where, is the stored energy of the storage element at time t, are the initial, maximum and minimum stored energies, Indicates the self-discharge rate of battery energy storage, and is the charging and discharging power of the battery energy storage, Charging power for the battery, is the battery discharge efficiency; △t is the scheduling time interval, and the following constraints are added to limit the operation of the energy storage element:

[0086]

[0087] At the same time, the capacity of the energy storage element is constrained to remain unchanged before and after the scheduling cycle.

[0088] Step 2: Classify the electrical load and thermal load according to their importance, use binary variables to control the order of removing loads of different importance, and establish a graded removal model of electrical and thermal loads under energy supply interruption.

[0089] It should be noted that power loads can be divided into primary, secondary, and tertiary loads based on power supply reliability and the losses or impacts caused by power outages. Primary loads generally refer to critical users or critical electrical equipment that have a significant impact on the power system. Secondary and tertiary loads are relatively less important and have a relatively smaller impact on the system. In the operation of multi-energy microgrids, islanding caused by extreme situations may force load shedding to maintain basic operations. In such cases, classifying loads and determining the order of load shedding based on their importance is a crucial strategy. This load shedding strategy, based on load importance, helps multi-energy microgrids respond to extreme situations more intelligently and efficiently, while reducing load shedding losses caused by faults and optimizing system resiliency.

[0090] In this embodiment, the electrical load and thermal load are divided into three levels according to their importance: primary load, secondary load, and tertiary load. Each level of load includes both electrical load and thermal load. Based on the load shedding mechanism of the multi-energy microgrid, a general load formula is established:

[0091]

[0092] Where Y is an auxiliary variable, Y = P represents the power grid, and Y = H represents the heating network; is the initial power at time t, is the amount of power removed at time t; is the load power at time t; Express the power of primary load, secondary load and tertiary load respectively; They represent the amount of removal for different levels of load;

[0093] definition is a binary variable, representing the removal status of the first-level load, the second-level load, and the third-level load, respectively. Different values ​​of the binary variable represent different load shedding scenarios, as shown in Table 1.

[0094] Table 1

[0095]

[0096] Furthermore, in order to meet the sequential removal of loads of different importance, the following constraints are established:

[0097]

[0098] Where, are the maximum values ​​of the corresponding loads respectively;

[0099] The above load formula and constraints constitute a hierarchical removal model of electrical and thermal load importance under energy supply interruption.

[0100] Since the existence of bilinear terms in the constraint expression is not conducive to calculation, a new continuous variable can be introduced And add the following constraints to rewrite the bilinear term in the formula:

[0101]

[0102] Among them, n∈1,2,3. is a continuous variable.

[0103] Step 3: Based on the load model of the multi-energy microgrid system and the hierarchical removal model of the importance of electric and thermal loads under energy supply interruption, a multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function.

[0104] Specifically, in this embodiment, the uncertainty of energy interruption time and photovoltaic output is taken into consideration, a corresponding uncertainty set is established, and the constraints of the multi-energy microgrid robust optimization model are set. The multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function.

[0105] Among them, the objective function of the multi-energy microgrid robust optimization model is:

[0106]

[0107] Where S is the total load reduction loss of the multi-energy microgrid during the power outage, A v 、B v are the loss weight parameters of electrical and thermal loads respectively.

[0108] The operating power constraints of the multi-energy microgrid robust optimization model include:

[0109]

[0110] in, is the input power of the grid at time t, is the maximum input power of the grid at time t. is the input power of the heating network at time t, is the maximum input thermal power of the heating network at time t; are all continuous variables in robust modeling. is the uncertainty parameter related to photovoltaic output, represents the output power of the photovoltaic panel at time t, is the heat release power of thermal energy storage, It is the charging power of thermal energy storage.

[0111] Preferably, the photovoltaic output uncertainty parameter satisfies the following uncertainty modeling formula:

[0112]

[0113] Where, is the expected value of photovoltaic active power output.

[0114] Considering the uncertainty of energy interruption time, the following uncertainty set is established:

[0115]

[0116] in, is the uncertain set of energy interruption time, and are integer variables, representing the additional time periods before and after the expected energy supply interruption start and end times, respectively; and They are and The maximum value of .

[0117] and Two integer variables are further defined as:

[0118]

[0119] in, and is a binary variable that represents the energy supply status during the additional time period and is defined by the following formula:

[0120]

[0121] in, The expected power grid or heat network interruption operation period, When it is 0, it indicates normal energy supply, and when it is 1, it indicates energy supply is disconnected.

[0122] The above parameters establish the fluctuation range of the power grid and heat network interruption time through the following constraints:

[0123]

[0124]

[0125] Where, and They represent the expected start and end time of the external network power supply interruption respectively. and is a binary variable representing the energy supply status during the additional time period. The subscript Y in the above parameters is an auxiliary variable. Replacing Y with P or H corresponds to the uncertainty model for the disconnection time of the power grid and the heat network, respectively. Furthermore, the following formula is added to the constraints of the robust optimization model of the multi-energy microgrid:

[0126]

[0127] The EMP solver of GAMS software is called to solve the multi-energy microgrid robust optimization model to obtain the optimal solution of the model.

[0128] In order to verify the effectiveness of the robust optimization method for multi-energy microgrids considering load importance classification provided by the embodiment of the present invention, a simulation is conducted on an actual integrated energy microgrid in a certain place as the research object. The parameter configuration of the actual integrated energy microgrid in a certain place is shown in Table 2. The simulation results are shown in Table 2. Figure 3 shown.

[0129] Table 2

[0130]

[0131]

[0132] The simulation results show that the present invention can realize sequential load shedding, reduce the load shedding loss of the multi-energy microgrid to a certain extent, improve the flexibility of the system, and be conducive to the economic and stable operation of the system.

[0133] The robust optimization method for a multi-energy microgrid that takes into account the importance of load grading provided by the present invention not only takes into account the interaction of various energies between multi-energy microgrids, including electrical energy and thermal energy, but also takes into account the uncertainty of renewable energy and energy supply interruption time, which is more in line with the actual operation of multi-energy microgrids. The present invention establishes a sequential shedding model for loads of different importance levels, and constructs a multi-energy microgrid robust optimization scheduling model that takes into account load grading and is elastic, with the goal of minimizing the load shedding level, thereby achieving a sequential load shedding effect in the event of an energy supply interruption, reducing the load shedding loss of the multi-energy microgrid, and improving the elasticity of the system. At the same time, it provides a reference for the design of the proportion of loads of different importance in future multi-energy microgrid systems and the load shedding system under extreme conditions.

[0134] Figure 4 The structural block diagram of the multi-energy microgrid robust optimization system considering load importance classification provided by the present invention is shown in FIG. Figure 4 The multi-energy microgrid robust optimization system 400 considering load importance classification includes:

[0135] A microgrid modeling module 401 is used to establish a load model of a multi-energy microgrid system; wherein the multi-energy microgrid system includes a photovoltaic generator set, a CHP unit, a gas boiler unit, an electric energy storage unit, and a thermal energy storage unit;

[0136] The load importance classification module 402 is used to classify the electrical load and the thermal load according to their importance, control the order of removing loads of different importance through binary variables, and establish a model for the importance classification and removal of electrical and thermal loads under power outages;

[0137] The microgrid optimization module 403 is configured to construct a robust optimization model for the multi-energy microgrid based on the load model of the multi-energy microgrid system and the hierarchical reduction model of electrical and thermal load importance under power outages, with minimizing the load reduction of the multi-energy microgrid system as the objective function.

[0138] The multi-energy microgrid robust optimization system considering load importance grading provided by the present invention executes the multi-energy microgrid robust optimization method considering load importance grading provided by the above-mentioned embodiments through the microgrid modeling module 401, the load importance grading module 402 and the microgrid optimization module 403. The multi-energy microgrid robust optimization method considering load importance grading has been described in detail in the above-mentioned embodiments, and will not be repeated here in this embodiment.

[0139] The application provides a multi-energy microgrid robust optimization system considering importance classification of loads, and establishes a sequential outage model of loads of different importance levels, constructs a multi-energy microgrid robust optimization scheduling model considering importance classification of loads and facing elasticity with a minimum load outage level as an objective, so that the sequential load shedding effect under power supply interruption is achieved, the load outage loss of the multi-energy microgrid is reduced, and the elasticity of the system is improved.

[0140] Figure 5 The structural block diagram of the electronic device provided by the application is shown in Figure 5 The electronic device 500 can be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and the like. The electronic device 500 comprises a processor 501 and a memory 502, wherein the memory 502 stores a multi-energy microgrid robust optimization program 505 considering importance classification of loads.

[0141] The memory 502 can be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device in some embodiments. The memory 502 can also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card and the like in other embodiments. Further, the memory 502 can comprise both the internal storage unit and the external storage device of the computer device. The memory 502 is used to store application software and various data installed in the computer device, such as program codes installed in the computer device. The memory 502 can also be used to temporarily store data that has been output or will be output. In an embodiment, the multi-energy microgrid robust optimization program 505 considering importance classification of loads is executed by the processor 501 to implement the following steps:

[0142] Step 1, establishing a load model of a multi-energy microgrid system; wherein the multi-energy microgrid system comprises a photovoltaic generator set, a CHP unit, a gas boiler unit, an electric energy storage unit and a thermal energy storage unit;

[0143] Step 2, classifying electric loads and thermal loads according to importance, controlling the outage sequence of loads of different importance by binary variables, and establishing an electric and thermal load importance classification outage model under power supply interruption;

[0144] Step 3: based on the load model of the multi-energy microgrid system and the electric and thermal load importance classification outage model under power supply interruption, constructing a multi-energy microgrid robust optimization model with the minimum load reduction of the multi-energy microgrid system as an objective function.

[0145] In some embodiments, the processor 501 can be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run the program code or process data stored in the memory 502, such as executing a multi-energy microgrid robust optimization program that takes into account load importance classification.

[0146] This embodiment further provides a computer-readable storage medium storing a multi-energy microgrid robust optimization program that considers load importance grading. When the multi-energy microgrid robust optimization program that considers load importance grading is executed by a processor, the following steps are implemented:

[0147] Step 1: Establish a load model for a multi-energy microgrid system; wherein the multi-energy microgrid system includes a photovoltaic generator set, a CHP unit, a gas boiler unit, an electric energy storage unit, and a thermal energy storage unit;

[0148] Step 2: Classify the electrical and thermal loads by importance, use binary variables to control the order of removing loads of different importance, and establish a hierarchical removal model for electrical and thermal loads under power outages.

[0149] Step 3: Based on the load model of the multi-energy microgrid system and the hierarchical removal model of the importance of electric and thermal loads under energy supply interruption, a multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function.

[0150] The above-described embodiments merely illustrate several embodiments of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of the present invention. Therefore, the scope of the present invention shall be determined by the appended claims.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A robust optimization method for multi-energy microgrids considering load importance classification, characterized by: include: Step 1: Establish a load model for a multi-energy microgrid system; wherein the multi-energy microgrid system includes a photovoltaic generator set, a CHP unit, a gas boiler unit, an electric energy storage unit, and a thermal energy storage unit; Step 2: Classify the electrical and thermal loads by importance, use binary variables to control the order of removing loads of different importance, and establish a hierarchical importance removal model for electrical and thermal loads under power outages; this includes: The electrical load and thermal load are divided into three levels according to their importance, including primary load, secondary load and tertiary load. Each level of load includes both electrical load and thermal load. The load general formula is established based on the load reduction mechanism of the multi-energy microgrid: Where Y is an auxiliary variable, Y = P represents the power grid, and Y = H represents the heating network; is the initial power of the power grid / heating network at time t, is the amount of power removed from the grid / heating network at time t; is the load power at time t; Express the power of primary load, secondary load and tertiary load respectively; They represent the amount of removal for different levels of load; definition is a binary variable, representing the removal status of the first-level load, the second-level load, and the third-level load, respectively. Different values ​​of the binary variable represent different scenarios of load shedding; In order to meet the sequential removal of loads of different importance, the following constraints are established: Where, are the maximum values ​​of different levels of load respectively; The load general formula and the constraint conditions constitute a hierarchical removal model of electrical and thermal load importance under energy supply interruption; Step 3: Based on the load model of the multi-energy microgrid system and the hierarchical removal model of the importance of electric and thermal loads under energy supply interruption, a multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function; The objective function of the multi-energy microgrid robust optimization model is: Where S is the total load reduction loss of the multi-energy microgrid during the power outage, A v 、B v are the loss weight parameters of electrical and thermal loads, is the amount of power removed from the heating network at time t, and ΩT is the time period set in the scheduling process; The operating power constraints of the multi-energy microgrid robust optimization model include: in, is the input power of the grid at time t, is the maximum input power of the grid at time t; is the input power of the heating network at time t, is the maximum input thermal power of the heating network at time t; are all continuous variables in robust modeling. is the uncertainty parameter related to photovoltaic output, is the initial power of the grid at time t, is the initial power of the heating network at time t, is the power removed from the grid at time t, represents the output power of the photovoltaic panel at time t, is the heat release power of thermal energy storage, It is the charging power of thermal energy storage.

2. The multi-energy microgrid robust optimization method considering load importance classification according to claim 1 is characterized in that: In step 1, the load model of the multi-energy microgrid system is established, including: The photovoltaic generator set of the multi-energy microgrid system is modeled as: Where, represents the output power of the photovoltaic panel at time t, f pv is the derating factor, P rpv is the rated installed capacity of the photovoltaic panels, Indicates the actual light intensity at time t, I STC is the light intensity under standard test conditions, v t is the wind speed, is the surface temperature of the photovoltaic panel, T STC is the standard test temperature, is the ambient temperature; The electrical output power and waste heat output power of the CHP unit are expressed by the following formula: The operating constraints of the CHP unit are expressed as follows: Where, are the electrical power and thermal power output of the CHP unit at time t, is the natural gas power consumed at time t, and are the power generation efficiency and heating efficiency of the CHP unit at time t, respectively, and BM is the large M coefficient; is the electric power parameter of the CHP unit, is the thermal power parameter of the CHP unit; Gas boiler units are used to convert natural gas into thermal energy and are responsible for peak load regulation. The thermal output power expression of gas boiler units is as follows: Where, is the heat output power of the gas boiler at time t, is the natural gas power input to the boiler unit at time t, COP bo The energy efficiency coefficient of converting natural gas into thermal energy; is the maximum heat output power of the gas boiler; The modeling formula for the electric energy storage unit and the thermal energy storage unit in the multi-energy microgrid system is: Where, and are the stored energy of the storage element at time t and time t-1, are the initial, maximum and minimum stored energies, Indicates the self-discharge rate of battery energy storage, and is the charging power and discharging power of the battery energy storage at time t, Charging power for the battery, is the battery discharge efficiency, △t is the scheduling time interval; add the following constraints to limit the operation of the energy storage element: Where, is the maximum charging power of the battery energy storage, The maximum discharge power of the battery energy storage.

3. The multi-energy microgrid robust optimization method considering load importance classification according to claim 2 is characterized in that: In step 3, the multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function, including: Considering the uncertainty of energy interruption time and photovoltaic output, the corresponding uncertainty set is established, and the constraints of the multi-energy microgrid robust optimization model are set. The multi-energy microgrid robust optimization model is constructed with the minimum load reduction of the multi-energy microgrid system as the objective function.

4. The robust optimization method for multi-energy microgrid considering load importance classification according to claim 3 is characterized in that: The photovoltaic output uncertainty parameter satisfies the following uncertainty modeling formula: Where, is the expected value of photovoltaic active power output; Considering the uncertainty of energy interruption time, the following uncertainty set is established: in, is the uncertain set of energy interruption time, and are integer variables, representing the additional time periods before and after the expected energy supply interruption start and end times, respectively; and They are and The maximum value of .

5. A multi-energy microgrid robust optimization system considering load importance classification, the system is used to execute the multi-energy microgrid robust optimization method considering load importance classification according to any one of claims 1 to 4, characterized in that: include: A microgrid modeling module is used to establish a load model for a multi-energy microgrid system; wherein the multi-energy microgrid system includes a photovoltaic generator set, a CHP unit, a gas boiler unit, an electric energy storage unit, and a thermal energy storage unit; The load importance classification module is used to classify electrical and thermal loads according to their importance, control the order of removing loads of different importance through binary variables, and establish a model for graded removal of electrical and thermal loads under power outages. The microgrid optimization module is used to construct a multi-energy microgrid robust optimization model based on the load model of the multi-energy microgrid system and the hierarchical removal model of the importance of electric and thermal loads under energy supply interruption, with the minimum load reduction of the multi-energy microgrid system as the objective function.

6. An electronic device, It is characterized by: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the multi-energy microgrid robust optimization method considering load importance classification as described in any one of claims 1 to 4 above.

7. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the multi-energy microgrid robust optimization method considering load importance classification as described in any one of claims 1 to 4 above.

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