Stage optimization system and method for island microgrid cluster

Abstract

The present application relates to a technical field, and discloses a stage optimization system and method for an island microgrid group. The system comprises: a group control center of the island microgrid group, a single control center of a single island microgrid, and island equipment; the single control center is used to perform a first stage scheduling of multiple island equipment in the single island microgrid according to a constructed first objective function in a first optimization stage to determine a first operating state of the island equipment in the single island microgrid; the group control center is used to receive and integrate the first operating states of multiple island equipment in the single island microgrid, and perform a second stage scheduling of the island equipment in the single island microgrid according to a constructed second objective function in a second optimization stage to determine a second operating state of the island equipment in the single island microgrid, which overcomes the lag of the centralized optimization method in the related art in adjusting the source and load changes of the microgrid, and enables the group control center of the island microgrid group to quickly calculate and obtain accurate scheduling instructions.

Description

Technical Field

[0001] The present application relates to the technical field of microgrid optimization and scheduling, and in particular to a stage optimization system and method for an island microgrid group. Background Art

[0002] Due to its unique geographical location and environmental conditions, islands face many challenges in energy supply. With the maturity of renewable energy technology, islands have begun to try to build microgrid systems based on solar and wind energy. The intermittent and instability of renewable energy in microgrid systems in islands affects the stable operation of island microgrids.

[0003] The centralized control architecture in related technologies can optimize the energy management of island microgrid groups. This centralized control method uses a centralized dispatching center to uniformly plan and dispatch the energy production, consumption and storage of each island microgrid. However, due to the intermittent and unstable nature of renewable energy, the dispatching results of the centralized control architecture are not accurate in actual operation and are difficult to adapt to the rapidly changing energy supply and demand conditions. Summary of the invention

[0004] The present application provides a stage optimization system and method for an island microgrid group, which solves the technical problem in the related art that the dispatch results are not accurate in actual operation, and achieves the technical effect of quickly calculating and obtaining accurate dispatch instructions.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a stage optimization system for an island microgrid group, including: a group control center of an island microgrid group, a single control center of a single island microgrid, and island equipment; the island microgrid group includes multiple single island microgrids, and the single island microgrid includes multiple island equipment; the multiple island equipment is communicatively connected to the single control center, and the single control center is communicatively connected to the group control center; the multiple island equipment includes one or more of photovoltaic equipment, wind turbine equipment, energy storage equipment, gas turbine equipment, and load equipment;

[0007] The single control center is used to perform the first-stage scheduling of the plurality of island devices in the single island microgrid according to the constructed first objective function in the first optimization stage to determine the first operating state of the island devices in the single island microgrid; wherein the first objective function is constructed with the goal of minimizing the weighted sum of operating resources and operating risks of the island devices in the single island microgrid; the operating resources include the island devices whose first operating state is the on state, and the operating risks include the conditional risk value and risk preference parameters of the island devices;

[0008] The group control center is used to receive and integrate the first operating states of the island devices in the multiple single island microgrids, and perform second-stage scheduling of the island devices in the single island microgrid according to the constructed second objective function in the second optimization stage to determine the second operating state of the island devices in the single island microgrid; wherein the second objective function is constructed with the goal of minimizing the operating resources of the island devices in the island microgrid group.

[0009] This application provides a stage optimization system for an island microgrid group. By constructing a single control center and a group control center, conditional risk value and risk preference parameters are introduced in the single control center, while considering the economy and safety of the stage optimization system. This enables the island microgrid group to independently select risk tolerance in different environments, quickly calculate accurate dispatch instructions, and maximize benefits while ensuring the safety and stability of the island microgrid group.

[0010] Then, a two-layer, two-stage stage optimization architecture of the island microgrid group system is constructed. Compared with the centralized optimization architecture in related technologies, it is more flexible and can perform random optimization in two stages. It overcomes the lag of the centralized optimization method in related technologies in adjusting the source and load changes of the microgrid, and enables the group control center of the island microgrid group to quickly calculate accurate dispatch instructions.

[0011] Optionally, the stage optimization system further comprises: a flexible power unit, through which the single control center is communicatively connected with the group control center;

[0012] The flexible power unit is used to receive the first operating state of the island equipment in the single island microgrid, and calculate the cumulative operating constraint data based on the first operating state, and send the cumulative operating constraint data to the island microgrid group control center, the cumulative operating constraint data includes the cumulative maximum slope rising potential, the cumulative maximum slope falling potential, the maximum dispatching power deviation and the minimum dispatching power deviation of the flexible power unit;

[0013] The group control center is used to receive and integrate the accumulated operating constraint data of multiple flexible power units, and perform second-stage scheduling of the island equipment in the single island microgrid according to the constructed second objective function in the second optimization stage to determine the second operating state of the island equipment in the single island microgrid; wherein the second objective function is constructed with the goal of minimizing the operating resources of the island equipment in the island microgrid group.

[0014] In this way, the first level of the optimized control architecture is proposed: single island microgrid and the second level: island microgrid group. In this way, the information exchange mechanism between the upper and lower levels is proposed. The single control center, that is, the flexible power unit provides the cumulative operation constraint data in the form of data intervals, which fuzzifies the deterministic data, reduces the privacy risk and maintains the efficiency of data communication. On the basis of protecting the information flow between the island microgrid group, the safe, stable and economic operation of the island microgrid group optimized control architecture is achieved.

[0015] Optionally, the stage optimization system further includes: power-to-gas equipment and other adjustable resources, wherein the power-to-gas equipment is connected between the natural gas pipeline network and the island microgrid group control center;

[0016] The group control center is used to receive and integrate the accumulated operation constraint data and power-to-gas equipment data of the plurality of flexible power units.

[0017] At the island microgrid group level, power-to-gas equipment and other adjustable resources are introduced, and the group control center is used to carry out two-stage random optimization with the goal of minimizing the consumption of operating resources for the entire system. The power-to-natural gas P2G and natural gas-to-electricity G2P processes are optimized to improve the flexibility and efficiency of the optimization system in the entire stage.

[0018] In a second aspect, an embodiment of the present application provides a stage optimization method for an island microgrid group, wherein the island microgrid group includes multiple single island microgrids, and the single island microgrid includes multiple island devices; the multiple island devices are communicatively connected to a single control center of the single island microgrid, and the single control center is communicatively connected to a group control center of the island microgrid group; the island devices include one or more of photovoltaic devices, wind turbine devices, energy storage devices, gas turbine devices, and load devices;

[0019] The method comprises: constructing a first objective function with the goal of minimizing the weighted sum of operating resources and operating risks of island equipment in the single island microgrid; constructing a second objective function with the goal of minimizing the operating resources of island equipment in the island microgrid group;

[0020] In the first optimization stage, the single control center performs the first stage scheduling of the plurality of island devices in the single island microgrid according to the first objective function to determine the first operating state of the island devices in the single island microgrid; wherein the operating resources include the island devices whose first operating state is the on state, and the operating risks include the conditional risk value and risk preference parameters of the island devices;

[0021] Receiving and integrating first operating states of island devices in a plurality of the single island microgrids;

[0022] In the second optimization stage, the group control center performs a second stage scheduling of the island devices in the single island microgrid according to the second objective function to determine a second operating state of the island devices in the single island microgrid.

[0023] The present application provides a stage optimization method for an island microgrid group. By introducing conditional risk value and risk preference parameters in the first optimization stage, the economic and risk factors are considered at the same time, allowing the island microgrid group to make a trade-off between operating resource consumption and operating risk. In the second optimization stage, the optimization is performed with the goal of minimizing the operating resources of the entire island microgrid group. A two-stage stage optimization method for an island microgrid group is implemented. Compared with the centralized optimization method in the related art, it is flexible and can perform random optimization in two stages. It overcomes the lag of the centralized optimization method in the related art in adjusting the source and load changes of the microgrid, and enables the group control center of the island microgrid group to quickly calculate accurate dispatch instructions.

[0024] Optionally, the receiving and integrating the first operating states of the island devices in the plurality of single island microgrids includes:

[0025] Receiving a first operating state of an island device in the single island microgrid, and calculating cumulative operation constraint data based on the first operating state;

[0026] The accumulated operation constraint data is sent to the group control center. In the second optimization stage, the group control center receives and integrates the accumulated operation constraint data of the island equipment in multiple single island microgrids; the accumulated operation constraint data includes the accumulated maximum slope rising potential, accumulated maximum slope falling potential, maximum dispatching power deviation and minimum dispatching power deviation of the flexible power unit.

[0027] In this way, the first level: single island microgrid and the second level: island microgrid group are proposed. In this way, the information exchange mechanism between the upper and lower levels is proposed. The single control center, that is, the flexible power unit provides the cumulative operation constraint data in the form of data interval, which blurs the deterministic data, reduces the privacy risk while maintaining the efficiency of data communication.

[0028] Optionally, performing first-stage scheduling of the plurality of island devices in the single island microgrid according to the first objective function in the first optimization stage includes:

[0029] In the first optimization stage, solving the first objective function based on the branch and bound method to obtain a first solution result;

[0030] Performing first-stage scheduling on the plurality of island devices in the single island microgrid based on the first solution result;

[0031] The second optimization stage performs the second stage scheduling of the island devices in the single island microgrid according to the second objective function, including:

[0032] In the second optimization stage, solving the second objective function based on the branch and bound method to obtain a second solution result;

[0033] Based on the second solution result, the island devices in the single island microgrid are scheduled in the second stage.

[0034] Optionally, a first constraint condition of the single control center is set, wherein the first constraint condition includes a supply-demand balance constraint, a main grid interaction constraint, a dispatchable power generation unit output constraint, an up and down ramp constraint, a start-stop state constraint, an energy storage charging and discharging constraint, and an adjustable load constraint;

[0035] A second constraint condition is set for the group control center, which includes power balance constraints, main grid power trading constraints, load reduction constraints, power change constraints of flexible power units, charging and discharging constraints of energy storage units, energy balance and capacity constraints of energy storage units, model constraints for interaction with the natural gas grid, and flexibility constraints.

[0036] Optionally, the first objective function is constructed according to the following formula:

[0037]

[0038] In the formula, is the first objective function, For the A single island microgrid in the scene The operational resources under is the expected value of the operating resource, is the risk attitude parameter of the single island microgrid control center, is the confidence level, At the confidence level Operational resources The conditional value at risk.

[0039] Optionally, the operating resources are calculated according to the following formula:

[0040]

[0041] In the formula, For the In a single island microgrid The generation resources of dispatchable generation units; For Moments and scenes Next In a single island microgrid The output power of dispatchable generating units; For the In a single island microgrid The resources for discharging energy storage devices; For Moments and scenes Next In a single island microgrid The discharge capacity of each energy storage device; For the Load reduction resources of a single island microgrid; For Moments and scenes Next Load reduction of a single island microgrid; For The main grid electricity price at the time; For Moments and scenes Next The main grid purchase power of a single island microgrid; For The main grid electricity price at the moment; For Moments and scenes Next The main grid electricity sales of a single island microgrid.

[0042] Optionally, constructing the second objective function according to the following formula includes:

[0043]

[0044] In the formula, is the second objective function, For the scene Probability of occurrence; For The island microgrid cluster adjusts the flexibility resources of its operation points at all times; For Moments and scenes Next, island microgrid The power variation of the flexible power unit; For island microgrid load shedding resources; for Moments and scenes The upper adjustment load reduction amount; exist The price of electricity purchased from the main grid at any given moment; For Moments and scenes Next, The amount of electricity purchased by each island microgrid from the main grid; For The price of electricity sold to the main grid at all times; For Moments and scenes Next, The amount of electricity sold by each island microgrid to the main grid; MGC energy storage unit Discharge resources; For Moments and scenes Next, MGC energy storage unit of discharge capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 Schematic diagram of a stage optimization system for an island microgrid group provided in an embodiment of the present application Figure 1 ;

[0047] Figure 2 Schematic diagram of a stage optimization system for an island microgrid group provided in an embodiment of the present application Figure 2 ;

[0048] Figure 3 A schematic diagram of the steps of a stage optimization method for an island microgrid group provided in an embodiment of the present application;

[0049] Figure 4 A schematic diagram of an island microgrid group in a verification experiment provided in an embodiment of the present application;

[0050] Figure 5 A schematic diagram of the expected output of a wind turbine device and a photovoltaic device provided in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of centralized optimization in the related technology provided in the embodiment of the present application;

[0052] Figure 7 Schematic diagram of the distribution of stage optimization provided in the embodiment of the present application Figure 1 ;

[0053] Figure 8Schematic diagram of the distribution of stage optimization provided in the embodiment of the present application Figure 2 ;

[0054] Fig. 9 An example diagram of a stage optimization method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0056] Due to its unique geographical location and environmental conditions, islands face many challenges in energy supply. With the maturity of renewable energy technology, islands have begun to try to build microgrid systems based on solar and wind energy. The intermittent and instability of renewable energy in microgrid systems in islands affects the stable operation of island microgrids.

[0057] The centralized control architecture in related technologies can optimize the energy management of island microgrid groups. This centralized control method uses a centralized dispatching center to uniformly plan and dispatch the energy production, consumption and energy storage of each island microgrid, in order to achieve the energy optimization configuration of the entire island microgrid group. However, the centralized control architecture may not be able to feedback the dispatching instructions in time due to the long calculation time, resulting in untimely and unstable energy supply.

[0058] Secondly, due to the intermittent and instability of renewable energy, the optimization results of centralized control may not be very accurate in actual operation, and it is difficult to adapt to the rapidly changing energy supply and demand conditions. At the same time, the risk tolerance of the island microgrid cannot be dynamically selected according to the actual situation.

[0059] In addition, the centralized control architecture may face issues of data security and privacy protection when processing large-scale data, especially in the scenario of island microgrid clusters. The data of each island microgrid needs to be aggregated and processed in the centralized control center, which may increase the risk of data leakage.

[0060] Reference Figure 1A stage optimization system for an island microgrid group includes: a group control center of the island microgrid group, a single control center of a single island microgrid and island equipment; the island microgrid group includes multiple single island microgrids, and the single island microgrid includes multiple island equipment; the multiple island equipment is communicatively connected to the single control center, and the single control center is communicatively connected to the group control center; the multiple island equipment includes one or more of photovoltaic equipment, wind turbine equipment, energy storage equipment, gas turbine equipment and load equipment.

[0061] A single control center is used to perform the first-stage scheduling of multiple island devices in a single island microgrid according to the constructed first objective function in the first optimization stage to determine the first operating state of the island devices in the single island microgrid; wherein the first objective function is constructed with the goal of minimizing the weighted sum of the operating resources and operating risks of the island devices in the single island microgrid; the operating resources include the island devices whose first operating state is the on state, and the operating risks include the conditional risk value and risk preference parameters of the island devices. The goal of the first objective function is to minimize the operating resources of the single island microgrid while introducing the conditional risk value (CVaR) and the risk preference parameter β to minimize the operating risk. Based on this objective function, a preliminary scheduling plan for the first stage of the island devices is obtained.

[0062] The group control center is used to receive and integrate the first operating status of island equipment in multiple single island microgrids, and perform second-stage scheduling of the island equipment in the single island microgrid according to the constructed second objective function in the second optimization stage to determine the second operating status of the island equipment in the single island microgrid; wherein the second objective function is constructed with the goal of minimizing the operating resources of the island equipment in the island microgrid group to meet the optimization goals at the island microgrid group level.

[0063] Specifically, a two-layer, two-stage stage optimization architecture of the island microgrid group system is established. Compared with the centralized optimization method in the relevant technology, it is flexible and can perform random optimization in two stages. In this stage optimization architecture, the main equipment includes photovoltaic equipment, wind turbine equipment, energy storage equipment, gas turbine equipment and load equipment. This equipment constitutes a single island microgrid, and a single control center performs optimal scheduling of the island equipment in a single island microgrid; multiple island microgrids are controlled by the group control center through radiation control, and the group control center is also connected to the electrical conversion equipment to connect the gas grid to the island microgrid group.

[0064] Mathematical modeling is performed on the island equipment in a single island microgrid. Considering the randomness and uncertainty of new energy and load, a probabilistic method is used in this application to describe its uncertainty in order to establish a probabilistic mathematical model of the island equipment.

[0065] Constructing the probabilistic mathematical model of wind turbine equipment in island equipment:

[0066] Wind speed is an important indicator of wind energy. Due to the different geographical locations and climatic conditions where wind turbines are installed, the output of wind turbines is greatly affected by wind speed. The wind speed of a wind power base obeys a two-parameter Weibull distribution, and the probability density function of wind speed is:

[0067]

[0068] Where v is the actual wind speed in m / s; k is the shape coefficient; λ is the wind speed shape coefficient.

[0069] The fan output power can be calculated from the wind speed:

[0070]

[0071] In the formula, The wind speed of the fan The output of is the air density, is the blade swept area, The wind speed of the fan The power coefficient under It is the minimum wind speed at which the fan starts to work. It is the wind speed at which the fan reaches its rated output.

[0072] The probability density function of wind turbine output is as follows:

[0073]

[0074] In the formula, Output for fan The probability density function of is the wind speed obtained by inverse solution of the fan performance curve; It is the derivative of wind turbine output with respect to wind speed.

[0075] Constructing a probabilistic mathematical model of photovoltaic equipment in island equipment:

[0076] The expression of the solar radiation model is as follows:

[0077]

[0078] In the formula, γ and γ max are the actual light intensity and the maximum light intensity respectively; Γ is the Gramma function, and is the shape factor of the Beta distribution curve.

[0079]

[0080]

[0081]

[0082] In the formula, λ is the integral variable; and are the mean and standard deviation of solar intensity in the area where the PV power station is located.

[0083] The photovoltaic panel efficiency model is constructed by the following formula:

[0084]

[0085] in, It's efficiency. is the temperature, is the radiation intensity, is the efficiency under standard test conditions, is the standard test temperature, and is the attenuation coefficient of temperature and radiation, It is the standard test radiation intensity.

[0086] The probability density function of photovoltaic output is as follows:

[0087]

[0088] Where A is the area of ​​the photovoltaic panel; It is the radiation intensity obtained by inverse solution of photovoltaic output P. It is the derivative of radiation intensity with respect to photovoltaic output.

[0089] The probabilistic mathematical model of load equipment in the island equipment is constructed as follows:

[0090] The load is regarded as a random variable, and the mixed distribution model is used to approximately reflect the uncertainty of the power load. The probability density function of the load can be expressed as:

[0091]

[0092] In the formula, is the probability density function of the mixture distribution, It is The mixing coefficient of the normal distribution satisfies ; It is The probability density function of a normal distribution is and They are The mean and variance of a normal distribution.

[0093] Constructing a probabilistic mathematical model of energy storage equipment in island equipment:

[0094] Energy storage devices have good response speed. In order to reduce the impact of uncertain factors in the integrated energy system, energy storage devices need to be introduced. Taking electric energy storage as an example, its mathematical model is established as follows:

[0095]

[0096] In the formula, and are the energy of the electric energy storage device in time period t and time period t+1, in Kw; and They are respectively the charging and discharging power of the energy storage, in kW. and Charging and discharging efficiency for electrical energy storage.

[0097] Constructing a probabilistic mathematical model of gas turbine equipment in island equipment:

[0098]

[0099] In the formula, , , are the electric power output, low-grade waste heat power and heat dissipation loss coefficient of the gas turbine at time t, and The unit is kW.

[0100] Constructing a probabilistic mathematical model of power-to-gas equipment in island microgrid clusters:

[0101]

[0102] In the formula, is the gas power generated by electricity-to-gas conversion during period t, in kW; is the conversion efficiency of P2G from electric power to gas power; is the electrical power consumed by P2G during period t, in kW.

[0103] High-resolution modeling of the island equipment in the island microgrid cluster was performed to ensure that the subsequent ramp constraints of the island equipment and the flexibility constraints of the main grid were met.

[0104] Furthermore, considering the uncertainty of power generation of new energy generators and the high correlation in time and space, in this application, Gaussian Copula function is used to simulate the correlation between power generation of renewable energy units in different island microgrids. Copula function helps to construct multivariable functions to simulate the correlation between random variables, and the following steps are used to generate a stage optimization architecture that considers the respective correlations.

[0105] S101, Random Dependence Measurement

[0106] Use rank correlation ( ) to measure the strength of the dependence between the corresponding decision variables. In this case, the rank correlation of the random variables X and Y, whose cumulative distribution functions (CDFs) are FX and FY respectively, are calculated as follows:

[0107] ρr(X,Y)=ρ(FX(X),FY(Y))

[0108] where ρ is a function that measures the linear correlation between FX(X) and FY(Y).

[0109] S102. Correlation Modeling Based on Copula

[0110] Use Gaussian Copula function C(u1,u2,...,uN) to describe the CDF function of its variables to simulate the multivariate joint distribution F(x1,x2,...,xN).

[0111] As shown below:

[0112]

[0113] S103, Generate scenes using K-Means clustering

[0114] In this step, a joint multivariate function is used to generate N scenarios in the [0,1]^N domain. Then, the inverse CDF function is used to transform the variables back to their respective original domains. Finally, the generated N scenarios are divided into S clusters with probability ρs based on the K-Means clustering process as the architecture preparation for the two-stage optimization of the island microgrid cluster system. The mathematical modeling of the island equipment in the single island microgrid is completed, which facilitates the subsequent smooth optimization of the single control center and the group control center.

[0115] This application provides a stage optimization system for an island microgrid group. By constructing a single control center and a group control center, conditional risk value and risk preference parameters are introduced in the single control center, while considering the economy and safety of the stage optimization system. This enables the island microgrid group to independently select risk tolerance in different environments, quickly calculate accurate dispatch instructions, and maximize benefits while ensuring the safety and stability of the island microgrid group.

[0116] Then, a two-layer, two-stage stage optimization architecture of the island microgrid group system is constructed. Compared with the centralized optimization architecture in related technologies, it is more flexible and can perform random optimization in two stages. It overcomes the lag of the centralized optimization method in related technologies in adjusting the source and load changes of the microgrid, and enables the group control center of the island microgrid group to quickly calculate accurate dispatch instructions.

[0117] The benefits are maximized while ensuring the safety and stability of the island microgrid group, and the privacy and security of data are guaranteed.

[0118] As an implementation method of this application, refer to Figure 2 ,The stage optimization system also includes: a flexible power unit through which the single control center is communicatively connected with the group control center;

[0119] The flexible power unit is used to receive the first operating state of the island equipment in the single island microgrid, calculate the accumulated operating constraint data based on the first operating state, and send the accumulated operating constraint data to the island microgrid group control center, the accumulated operating constraint data including the accumulated maximum slope rising potential, accumulated maximum slope falling potential, maximum dispatching power deviation and minimum dispatching power deviation of the flexible power unit;

[0120] The group control center is used to receive and integrate the accumulated operating constraint data of multiple flexible power units, and in the second optimization stage, perform the second stage scheduling of the island equipment in the single island microgrid according to the constructed second objective function to determine the second operating state of the island equipment in the single island microgrid; wherein the second objective function is constructed with the goal of minimizing the operating resources of the island equipment in the island microgrid group.

[0121] Specifically, in the process of the above-mentioned information transmission, the operating data of the island equipment in the single island microgrid, namely the operating point optimized in the first phase, the maximum power production and the minimum power production, as well as the slope constraint, are directly transmitted. These operating data need to be sent to the group control center for the second phase optimization and coordination of the scheduling of the island equipment in the island microgrid group.

[0122] In this embodiment, a flexible power unit is added, so that the information exchange between the single control center and the group control center is limited to the cumulative operation constraints of the flexible power unit for privacy protection. The cumulative operation constraint data includes the cumulative maximum slope up potential, the cumulative maximum slope down potential, the maximum dispatch power deviation and the minimum dispatch power deviation of the flexible power unit. At the same time, the data communication volume between them is kept consistent with the previously proposed method, that is, this embodiment ensures data sharing under the premise of protecting privacy by designing an information exchange protocol in the flexible power unit.

[0123] The cumulative maximum ramp-up potential of flexible power units in the cumulative operating constraint data is calculated as follows:

[0124]

[0125] In the formula, For Moments and scenes Next, island microgrid The cumulative maximum ramp-up potential of the flexible power units. Flexible power unit Microgrid on an island The maximum ascent gradient limit in. For Moments and scenes Next, flexible power unit The dispatching power. For the previous time and scenes Next, flexible power unit The dispatching power.

[0126] The cumulative maximum ramp-down potential of the flexible power units in the cumulative operating constraint data is calculated as follows:

[0127]

[0128] In the formula, For Moments and scenes Next, island microgrid The cumulative maximum slope-down potential of the flexible power units; Flexible power unit Microgrid on an island The maximum descent slope limit in.

[0129] The maximum dispatch power deviation of the flexible power unit in the cumulative operation constraint data is calculated by the following formula:

[0130]

[0131] In the formula, For Moments and scenes Next, island microgrid The maximum dispatch power deviation of the flexible power unit. Flexible power unit Microgrid on an island The maximum dispatch power in .

[0132] The minimum dispatch power deviation of the flexible power unit in the cumulative operation constraint data is calculated by the following formula:

[0133]

[0134] In the formula, Respectively in Moments and scenes Next, island microgrid The minimum dispatch power deviation of the flexible power unit. Flexible power unit Microgrid on an island The minimum dispatching power in .

[0135] As an implementation method of the present application, the stage optimization system further includes: power-to-gas equipment and other adjustable resources, the power-to-gas equipment being connected between the natural gas pipeline network and the control center of the island microgrid group;

[0136] A group control center that receives and integrates the accumulated operating constraint data and power-to-gas equipment data of multiple flexible power units.

[0137] At the island microgrid group level, power-to-gas equipment and other adjustable resources are introduced, and the group control center is used to carry out two-stage random optimization with the goal of minimizing the consumption of operating resources for the entire system. The power-to-natural gas P2G and natural gas-to-electricity G2P processes are optimized to improve the flexibility and efficiency of the optimization system in the entire stage.

[0138] This application provides a stage optimization method for an island microgrid group. Please continue to refer to Figure 1 The island microgrid group includes multiple single island microgrids, and the single island microgrid includes multiple island devices; the multiple island devices are communicatively connected to the single control center of the single island microgrid, and the single control center is communicatively connected to the group control center of the island microgrid group; the island devices include one or more of photovoltaic, wind turbine equipment, energy storage equipment, gas turbine equipment and load equipment;

[0139] Specifically, the present application provides a two-layer two-stage optimization method, in which the lower layer of the optimization framework is composed of a single island microgrid consisting of photovoltaic equipment, wind turbine equipment, gas turbine equipment, energy storage equipment, load equipment and other equipment, which is controlled by a single control center; the upper layer of the optimization framework is composed of a gas grid and other adjustable equipment, which is controlled by a group control center.

[0140] Reference Figure 3 , the stage optimization methods include:

[0141] S100. Constructing a first objective function with the goal of minimizing the weighted sum of operating resources and operating risks of island equipment in a single island microgrid to optimize the scheduling of island equipment on the second day. Furthermore, at the level of a single island microgrid, the scheduling optimization cycle can be one hour, one minute, or one week, etc.; constructing a second objective function with the goal of minimizing the operating resources of island equipment in an island microgrid group.

[0142] S200. In the first optimization stage, the single control center performs the first stage scheduling of multiple island devices in the single island microgrid according to the first objective function to determine the first operating state of the island devices in the single island microgrid; wherein the operating resources include the island devices whose first operating state is the on state, and the operating risks include the conditional risk value and risk preference parameters of the island devices;

[0143] S300, receiving and integrating the first operating states of the island devices in multiple single island microgrids;

[0144] S400. In the second optimization stage, the group control center performs a second stage scheduling of the island devices in the single island microgrid according to the second objective function to determine the second operating state of the island devices in the single island microgrid.

[0145] Furthermore, the single control center schedules and optimizes the operating status of the island equipment in the single island microgrid on the next day.

[0146] The present application provides a stage optimization method for an island microgrid group. By introducing conditional risk value and risk preference parameters in the first optimization stage, the economic and risk factors are considered at the same time, allowing the island microgrid group to make a trade-off between operating resource consumption and operating risk. In the second optimization stage, the optimization is performed with the goal of minimizing the operating resources of the entire island microgrid group. A two-stage stage optimization method for an island microgrid group is implemented. Compared with the centralized optimization method in the related art, it is flexible and can perform random optimization in two stages. It overcomes the lag of the centralized optimization method in the related art in adjusting the source and load changes of the microgrid, and enables the group control center of the island microgrid group to quickly calculate accurate dispatch instructions.

[0147] As an implementation method of this application, please continue to refer to Figure 2 , receiving and integrating the first operating status of the island devices in the plurality of single island microgrids, including:

[0148] S310, receiving a first operating state of an island device in a single island microgrid, and calculating cumulative operation constraint data based on the first operating state;

[0149] S320. Send the accumulated operation constraint data to the group control center. In the second optimization stage, the group control center receives and integrates the accumulated operation constraint data of the island equipment in multiple single island microgrids; the accumulated operation constraint data include the accumulated maximum slope rising potential, accumulated maximum slope falling potential, maximum dispatching power deviation and minimum dispatching power deviation of the flexible power unit.

[0150] This embodiment proposes the first level: a single island microgrid and the second level: an island microgrid group. The information exchange mechanism between the upper and lower levels provides a data interval in the form of accumulated operation constraint data, which blurs the deterministic data and reduces privacy risks while maintaining the efficiency of data communication.

[0151] As an implementation mode of the present application, in the first optimization stage, a plurality of island devices in a single island microgrid are scheduled in the first stage according to the first objective function, including:

[0152] S210, in a first optimization stage, solving a first objective function based on a branch and bound method to obtain a first solution result;

[0153] S220, performing first-stage scheduling of multiple island devices in the single island microgrid based on the first solution result;

[0154] In the second optimization stage, the second stage scheduling of the island equipment in the single island microgrid is performed according to the second objective function, including:

[0155] S410, in a second optimization stage, solving a second objective function based on a branch and bound method to obtain a second solution result;

[0156] S420. Perform a second-stage scheduling of the island devices in the single island microgrid based on the second solution result.

[0157] Specifically, considering that the first objective function, the second objective function, the first constraint and the second constraint can all be expressed linearly and contain integer variables, the optimization problem of the island microgrid is a typical mixed integer linear programming problem. The optimization problem containing a large number of integer variables is a non-deterministic problem of polynomial complexity. This application introduces the branch and bound method in integer programming to solve this problem.

[0158] The core idea of ​​the solution is to transform the problem into multiple linear programming problems that can be solved in polynomial time, and transform the original problem into a linear relaxation problem. In the process of solving the linear relaxation problem, the upper and lower bounds of the original problem, that is, the optimal feasible solution and the optimal relaxation solution, are determined as follows:

[0159] S211, set the lower bound of the original problem objective function to , set the upper bound of the original problem objective function to , linearly relax the integer variable into a continuous variable, that is, take the value [0,1], solve the relaxed two-stage scheduling model, and obtain the corresponding optimal solution. If the optimal solution satisfies the integer constraint condition, it is the optimal solution to the original problem; if it does not satisfy the integer constraint condition, execute step S213.

[0160] S213, assign the objective function corresponding to the solution to LB. For integer variables that do not satisfy the integer constraint conditions, select an integer variable and assign it to 0 or 1, that is, the branching process, and add it to the relaxed two-stage scheduling model for solution. In the branching process, if the solution to the problem after relaxation is a feasible solution to the original problem, that is, the branch satisfies the integer constraint conditions, then assign the objective function corresponding to the solution to UB.

[0161] S215. When solving the branch relaxation problem, if LB>UB, or the node relaxation problem has no solution, the node and the branches below it can be pruned, that is, there is no need to continue solving the following relaxation problem.

[0162] S217, convergence judgment, when the optimal solution obtained satisfies the integer condition and (UB-LB) / LB< hour, If is the preset convergence value, the current best feasible solution (UB) is the global optimal solution to the original problem, and the algorithm converges. Otherwise, the process returns to repeat step S213.

[0163] As an implementation mode of the present application, a first constraint condition of a single control center is set, that is, the first constraint condition of a single island microgrid, also called an island microgrid, and the first constraint condition includes supply and demand balance constraints, main grid interaction constraints, dispatchable power generation unit output constraints, up and down climbing constraints, start and stop state constraints, energy storage charging and discharging constraints, and adjustable load constraints.

[0164] Specifically, the supply and demand balance constraint in the first constraint condition is as follows:

[0165]

[0166] In the formula, the left side is the power generation input by a single control center, and the right side is the power generation output by a single control center. For Moments and scenes Next, Among the island microgrids, The output power of dispatchable generating units; For Moments and scenes Next, Among the island microgrids, The output power of a dispatchable generating unit. For Moments and scenes Next, The first The discharge capacity of an energy storage unit. For Moments and scenes Next, Load reduction of an island microgrid. For Moments and scenes Next, The amount of electricity purchased by the island microgrid from the main grid. For Moments and scenes Next, The first load; For Moments and scenes Next, The first The charge capacity of each energy storage unit; For Moments and scenes Next, The amount of electricity sold by an island microgrid to the main grid.

[0167] The mainnet interaction constraints in the first constraint condition are as follows:

[0168] In the formula, For Moment, The minimum amount of electricity that an island microgrid must purchase from the main grid; For Moment, The maximum amount of electricity that an island microgrid can purchase from the main grid; For Moment, The amount of electricity purchased by the island microgrid from the main grid.

[0169]

[0170] In the formula, For Moment, The minimum amount of electricity that an island microgrid can sell to the main grid; For Moment, The maximum amount of electricity sold by an island microgrid to the main grid; For Moment, The amount of electricity sold by an island microgrid from the main grid.

[0171] The output constraints of dispatchable power generation units in the first constraint condition are as follows:

[0172]

[0173] In the formula, the power generation unit includes one or more of photovoltaic equipment, wind turbine equipment, energy storage equipment, and gas turbine equipment. , They are respectively the dispatchable power generation unit 𝑖 in the island microgrid The minimum and maximum power generation in . is a binary variable, if the dispatchable generating unit In time and scenes 1 if the function is running, 0 otherwise.

[0174] The up and down climbing constraints of the power generation unit in the first constraint condition are as follows:

[0175]

[0176] In the formula, For the time and scene at 𝑡 Next, Among the island microgrids, The output power of dispatchable generating units; For Moments and scenes Next, Among the island microgrids, The output power of dispatchable generating units; For the Among the island microgrids, The maximum ramp limit of a dispatchable generating unit.

[0177]

[0178] Dispatchable power generation unit Microgrid on an island The maximum descent limit in .

[0179] The start-stop state constraints in the first constraint condition include the minimum start-up time constraint, the minimum stop time constraint, the start-up restriction and the shutdown restriction. The start-stop state constraints of the power generation unit are as follows:

[0180] The minimum power-on time constraint is calculated by the following formula:

[0181]

[0182] In the formula, For in time and scenes Next, unit The number of hours the system has been running continuously. For the Minimum start-up time of each power generation equipment; Respectively expressed in time and time Next, island microgrid Middle Unit A binary variable indicating whether the function is running (1 for running, 0 for not running).

[0183] The minimum downtime constraint is calculated by the following formula:

[0184]

[0185] In the formula, For in time and scenes Next, unit The number of consecutive hours of outage; Indicates Minimum downtime of power generation.

[0186] Startup restriction is performed by the following formula:

[0187]

[0188] In the formula, Indicates at time and scenes Next, island microgrid Middle Unit Binary variable indicating the startup status of the .

[0189] The closing restriction is performed by:

[0190]

[0191] In the formula, For in time and scenes Next, island microgrid Middle Unit A binary variable indicating the closed state of the

[0192] The cumulative limit of the start-stop state is performed by the following formula:

[0193]

[0194] The energy storage charging and discharging constraints in the first constraint condition are as follows:

[0195]

[0196]

[0197]

[0198]

[0199] In the formula, , Respectively The first The minimum and maximum discharge capacity of each energy storage unit; , Respectively The first The minimum and maximum charge capacity of each energy storage unit; For the The energy storage unit is In the island microgrid, and scenes The stored energy under. For the The energy storage unit is In the island microgrid, and scenes The stored energy under. , Energy storage unit Microgrid on an island Charging and discharging efficiency in Energy storage unit Microgrid on an island The minimum and maximum storage capacity in .

[0200] The adjustable load constraint in the first constraint condition is as follows:

[0201]

[0202]

[0203] In the formula, , Island Microgrid The minimum and maximum values ​​of the adjustable load;

[0204] For time and scenes Next, island microgrid The demand of adjustable load; To ensure that the island microgrid The total energy demand of the adjustable load.

[0205] The second constraint conditions of the group control center are set, which include power balance constraints, main grid power trading constraints, load reduction constraints, power change constraints of flexible power units, charging and discharging constraints of energy storage units, energy balance and capacity constraints of energy storage units, model constraints for interaction with the natural gas grid, and flexibility constraints for interaction with the natural gas grid.

[0206] Specifically, the power balance constraint in the second constraint condition is as follows:

[0207]

[0208] In the formula, For in time and scenes Next, The power variation of the flexible power unit of an island microgrid. For in time and scenes Next, Load reduction of each island microgrid; For in time and scenes Next, The amount of electricity purchased by an island microgrid from the main grid. For in time and scenes Next, The amount of electricity sold by an island microgrid to the main grid. For in time and scenes Next, the island microgrid The discharge capacity of an energy storage unit. In time and scenes Next, Power generation of each P2G unit. For in time and scenes Next, MGC The charge capacity of an energy storage unit. For the The island microgrid is and scenes The power short port below; Excess electricity.

[0209] The main grid power transaction constraints in the second constraint condition are as follows:

[0210]

[0211] In the formula, , Respectively at time Under this condition, the minimum and maximum values ​​of the amount of electricity purchased by the entire island microgrid system from the main grid are as follows:

[0212]

[0213] In the formula, , Respectively at time Under this condition, the minimum and maximum values ​​of the amount of electricity sold by the entire island microgrid system to the main power grid.

[0214] The load reduction constraint in the second constraint is as follows:

[0215]

[0216] In the formula, For in time and scenes Next, The adjusted load reduction amount of each island microgrid is the actual load reduction. For in time and scenes Next, The power shortage of an island microgrid is the amount of power supply that is insufficient to meet demand.

[0217] The power change constraint of the flexible power unit in the second constraint is as follows:

[0218]

[0219] In the formula, For: At the last time point and scenes Next, The power variation of each island microgrid flexible power unit; For in time and scenes Next, The maximum downward slope change (i.e., the amount of power reduction) allowed for an island microgrid flexible power unit. For in time and scenes Next, The maximum upward slope change (i.e., the amount of power added) allowed for each island microgrid flexible power unit; For in time and scenes Next, The current power change of the flexible power unit of the island microgrid.

[0220]

[0221] In the formula, , Respectively at time and scenes Next, The minimum or maximum power change of each island microgrid flexible power unit.

[0222] The charging and discharging constraints of the energy storage unit in the second constraint condition are as follows:

[0223]

[0224]

[0225] In the formula, , Respectively The minimum or maximum discharge capacity of each MGC energy storage unit; , Respectively The minimum or maximum charge capacity of an MGC energy storage unit.

[0226] The energy balance and capacity constraints of the energy storage unit in the second constraint are as follows:

[0227]

[0228] In the formula, For in time and scenes Next, The estimated energy storage capacity of each MGC energy storage unit. For in time and scenes Next, The estimated energy storage capacity of each MGC energy storage unit. For in time and scenes Next, The charging power of an MGC energy storage unit. For the The charging efficiency of each MGC energy storage unit. For in time and scenes Next, The discharge power of an MGC energy storage unit. For the Discharge efficiency of an MGC energy storage unit.

[0229]

[0230] In the formula, , Respectively The minimum or maximum storage capacity limit of each MGC energy storage unit.

[0231] The model constraints for interaction with the natural gas grid in the second constraint condition are as follows:

[0232]

[0233] In the formula, For in time and scenes Energy stored in the natural gas grid. For in time and scenes Energy stored in the natural gas grid. For in time and scenes The power injected into the natural gas grid. For in time and scenes The power extracted from the natural gas grid.

[0234]

[0235]

[0236]

[0237] In the formula, For the The efficiency of each P2G unit. For in time and scenes Next, The power of each P2G unit; , the minimum or maximum limit, respectively, on the power injected into the gas grid; , The minimum or maximum limit, respectively, on the power drawn from the natural gas grid.

[0238] The flexibility constraints in the second constraint condition are as follows:

[0239]

[0240] In the formula, For the scene Probability of occurrence. For in time and scenes Next, The amount of electricity purchased from the main grid by an island microgrid after upward adjustment. For in time and scenes Next, The amount of electricity sold to the main grid after an island microgrid is adjusted upward. For in time The allowed positive flexibility ramp limit is the maximum net load increase the system can sustain at that point in time.

[0241] As an implementation method of the present application, the first objective function is constructed according to the following formula:

[0242]

[0243] In the formula, is the first objective function, For the A single island microgrid in the scene The operational resources under is the expected value of the operating resource, is the risk attitude parameter of the single island microgrid control center, is the confidence level, At the confidence level Operational resources The conditional value at risk is the operational risk.

[0244] As an implementation method of this application, operating resources Including the operating resources of dispatchable power generation units, the discharge resources of energy storage equipment, load reduction resources and power trading resources with the main grid, the operating resources are calculated according to the following formula:

[0245]

[0246] In the formula, For the In a single island microgrid The generation resources of dispatchable generation units; For Moments and scenes Next In a single island microgrid The output power of dispatchable generating units; For the In a single island microgrid The resources for discharging energy storage devices; For Moments and scenes Next In a single island microgrid The discharge capacity of each energy storage device; For the Load reduction resources of a single island microgrid; For Moments and scenes Next Load reduction of a single island microgrid; For The main grid electricity price at the time; For Moments and scenes Next The main grid purchase power of a single island microgrid; For The main grid electricity price at the moment; For Moments and scenes Next The main grid electricity sales of a single island microgrid.

[0247] As an implementation mode of the present application, the second objective function is constructed according to the following formula, including:

[0248]

[0249] In the formula, is the second objective function, For the scene Probability of occurrence; For The island microgrid cluster adjusts the flexibility resources of its operation points at all times; For Moments and scenes Next, island microgrid The power variation of the flexible power unit; For island microgrid load shedding resources; for Moments and scenes The upper adjustment load reduction amount; exist The price of electricity purchased from the main grid at any given moment; For Moments and scenes Next, The amount of electricity purchased by each island microgrid from the main grid; For The price of electricity sold to the main grid at all times; For Moments and scenes Next, The amount of electricity sold by each island microgrid to the main grid; MGC energy storage unit Discharge resources; For Moments and scenes Next, MGC energy storage unit of discharge capacity.

[0250] Verification experiment

[0251] Reference Figure 4 , consisting of three island microgrids, natural gas pipelines, energy storage equipment and other dispatchable equipment, the island microgrid group includes photovoltaic equipment, load equipment, energy storage equipment and gas turbine equipment in island microgrids 1, 2 and 3. The relevant parameters of the input load equipment, energy storage equipment and other distributed power sources are set according to the actual parameters.

[0252] The purchase price and sales price of electricity vary in different time periods of the day. The purchase price of electricity from 23:00 to 8:00 is 0.47 yuan / kWh, and the sales price is 0.32 yuan / kWh; the purchase price of electricity from 8:00 to 17:00 is 0.69 yuan / kWh, and the sales price is 0.48 yuan / kWh; the purchase price of electricity from 17:00 to 23:00 is 0.80 yuan / kWh, and the sales price is 0.70 yuan / kWh.

[0253] Set = 0.95, β = 0, use the centralized optimization and two-stage optimization methods to compare, and get the comparison results. Figures 5 to 8 , Figure 5 This is a schematic diagram of the expected output of wind turbine equipment and photovoltaic equipment. Figure 6 It is the result of concentrated optimization in related technologies. Figure 7 and Figure 8 is the result of two-stage optimization of the method of this application, wherein, Figure 7 This is the result of the first stage optimization of this application method. Figure 8 This is the result of the second stage optimization of the method of this application. The similarities between the two optimization methods are that they both buy electricity from the main grid when the electricity price is low and sell electricity to the grid when the electricity price is high. The difference is that the centralized optimization is a one-time optimization, which cannot make timely adjustments to the source and load changes, resulting in the system's operating resources being 6.1% higher than the two-stage optimization method, and the calculation time is also more than 1.6 minutes of the two-stage optimization method, reaching 4.5 minutes, as shown in Table 1.

[0254] At the same time, the risk aversion coefficient of the island microgrid group is changed. It can be seen from Table 1 that with the increase of the risk aversion coefficient, the island microgrid group tends to be conservative. By increasing the operating resources, the island microgrid group is in a safe and stable operation state.

[0255] Table 1 Comparison of optimization results under different optimization methods with different risk aversion coefficients

[0256]

[0257] As an implementation method of this application, refer to Fig. 9 , an example diagram of a stage optimization method is provided, the method comprising:

[0258] S510, perform the first phase optimization in the single island microgrid control system, the first phase optimization includes:

[0259] S512. Inputting the first-stage optimization data, the first-stage optimization data includes the power exchange price with the main power grid and the local resource power information, the local resource power information includes adjustable resources and renewable resources, etc.

[0260] S514. Optimize local resource scheduling and send scheduling data to the multi-island microgrid control center.

[0261] S520, performing the second phase optimization at the multi-island microgrid control center, the second phase optimization includes:

[0262] S522. Input the second-stage optimization data, which includes the electricity price and flexible resource ramping constraints of the main power grid, the operation data of other flexible resources in the multi-microgrid, and the preliminary dispatching data of each island microgrid after the first-stage optimization.

[0263] S524. Consider optimizing the multi-island microgrid system in a way that minimizes operating costs.

[0264] To sum up, it can be seen that the method proposed in the embodiment of the present application can be flexibly adjusted according to the characteristics of the island microgrid group itself to improve the risk resistance of the island microgrid group, and quickly calculate accurate scheduling instructions, thereby maximizing benefits while ensuring the safety and stability of the island microgrid group, while ensuring the privacy and security of data.

[0265] The method proposed in the embodiment of the present application is suitable for optimizing the scheduling tasks for multiple island devices in an island microgrid group when the source and load of the island microgrid are uncertain, effectively improving the risk resistance and economy of the island microgrid group, and providing a stage optimization method with strong risk resistance, low resource consumption and strong information protection, which has important innovation and practical value.

[0266] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0267] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0268] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A stage optimization system for an island microgrid group, characterized in that: include: A group control center of an island microgrid group, a single control center of a single island microgrid, an island device and a flexible power unit; the island microgrid group includes multiple single island microgrids, and the single island microgrid includes multiple island devices; the multiple island devices are communicatively connected to the single control center, the single control center is communicatively connected to the group control center, and the single control center is communicatively connected to the group control center through the flexible power unit; the multiple island devices include one or more of photovoltaic devices, wind turbine devices, energy storage devices, gas turbine devices and load devices; The single control center is used to perform the first-stage scheduling of the plurality of island devices in the single island microgrid according to the constructed first objective function in the first optimization stage to determine the first operating state of the island devices in the single island microgrid; wherein the first objective function is constructed with the goal of minimizing the weighted sum of operating resources and operating risks of the island devices in the single island microgrid; the operating resources include the island devices whose first operating state is the on state, and the operating risks include the conditional risk value and risk preference parameters of the island devices; The group control center is used to receive and integrate the first operating states of the island devices in the plurality of single island microgrids, and to perform second-stage scheduling of the island devices in the single island microgrid according to the constructed second objective function in the second optimization stage to determine the second operating state of the island devices in the single island microgrid; wherein the second objective function is constructed with the goal of minimizing the operating resources of the island devices in the island microgrid group; The flexible power unit is used to receive the first operating state of the island equipment in the single island microgrid, and calculate the accumulated operating constraint data based on the first operating state, and send the accumulated operating constraint data to the group control center of the island microgrid, wherein the accumulated operating constraint data includes the accumulated maximum slope rising potential, the accumulated maximum slope falling potential, the maximum dispatching power deviation and the minimum dispatching power deviation of the flexible power unit; The group control center is used to receive and integrate the accumulated operating constraint data of multiple flexible power units, and perform second-stage scheduling of the island equipment in the single island microgrid according to the constructed second objective function in the second optimization stage to determine the second operating state of the island equipment in the single island microgrid; wherein the second objective function is constructed with the goal of minimizing the operating resources of the island equipment in the island microgrid group.

2. The system according to claim 1, characterized in that The stage optimization system also includes: power-to-gas equipment and other adjustable resources, wherein the power-to-gas equipment is connected between the natural gas pipeline network and the control center of the island microgrid group; The group control center is used to receive and integrate the accumulated operation constraint data and power-to-gas equipment data of the plurality of flexible power units.

3. A stage optimization method for an island microgrid group, characterized in that: The island microgrid group includes a plurality of single island microgrids, and the single island microgrid includes a plurality of island devices; the plurality of island devices are communicatively connected to a single control center of the single island microgrid, and the single control center is communicatively connected to a group control center of the island microgrid group; the island devices include one or more of photovoltaic devices, wind turbine devices, energy storage devices, gas turbine devices, and load devices; The method comprises: constructing a first objective function with the goal of minimizing the weighted sum of operating resources and operating risks of island equipment in the single island microgrid; constructing a second objective function with the goal of minimizing the operating resources of island equipment in the island microgrid group; In the first optimization stage, the single control center performs the first stage scheduling of the plurality of island devices in the single island microgrid according to the first objective function to determine the first operating state of the island devices in the single island microgrid; wherein the operating resources include the island devices whose first operating state is the on state, and the operating risks include the conditional risk value and risk preference parameters of the island devices; Receive and integrate the first operating status of the island devices in the single island microgrid; specifically, receive the first operating status of the island devices in the single island microgrid, and calculate the cumulative operating constraint data based on the first operating status; send the cumulative operating constraint data to the group control center, and in the second optimization stage, the group control center receives and integrates the cumulative operating constraint data of the island devices in the single island microgrid; the cumulative operating constraint data includes the cumulative maximum slope rising potential, the cumulative maximum slope falling potential, the maximum dispatching power deviation and the minimum dispatching power deviation of the flexible power unit; In the second optimization stage, the group control center performs a second stage scheduling of the island devices in the single island microgrid according to the second objective function to determine a second operating state of the island devices in the single island microgrid.

4. The method according to claim 3, characterized in that In the first optimization stage, the first stage scheduling of the plurality of island devices in the single island microgrid is performed according to the first objective function, including: In the first optimization stage, solving the first objective function based on the branch and bound method to obtain a first solution result; Performing first-stage scheduling on the plurality of island devices in the single island microgrid based on the first solution result; In the second optimization stage, the island devices in the single island microgrid are scheduled in the second stage according to the second objective function, including: In the second optimization stage, solving the second objective function based on the branch and bound method to obtain a second solution result; Based on the second solution result, the island devices in the single island microgrid are scheduled in the second stage.

5. The method according to claim 3, characterized in that: Setting a first constraint condition for the single control center, the first constraint condition including supply and demand balance constraint, main grid interaction constraint, dispatchable power generation unit output constraint, up and down ramp constraint, start and stop state constraint, energy storage charging and discharging constraint and adjustable load constraint; A second constraint condition is set for the group control center, which includes power balance constraints, main grid power trading constraints, load reduction constraints, power change constraints of flexible power units, charging and discharging constraints of energy storage units, energy balance and capacity constraints of energy storage units, model constraints for interaction with the natural gas grid, and flexibility constraints.

6. The method according to claim 3, characterized in that The first objective function is constructed according to the following formula: In the formula, is the first objective function, For the A single island microgrid in the scene The operational resources under is the expected value of the operating resource, is the risk attitude parameter of the single island microgrid control center, is the confidence level, At the confidence level Operational resources The conditional value at risk.

7. The method according to claim 6, characterized in that The operating resources are calculated according to the following formula: In the formula, For the In a single island microgrid The generation resources of dispatchable generation units; For Moments and scenes Next In a single island microgrid The output power of dispatchable generating units; For the In a single island microgrid The resources for discharging energy storage devices; For Moments and scenes Next In a single island microgrid The discharge capacity of each energy storage device; For the Load reduction resources of a single island microgrid; For Moments and scenes Next Load reduction of a single island microgrid; For The main grid electricity price at the time; For Moments and scenes Next The main grid purchase power of a single island microgrid; For The main grid electricity price at the moment; For Moments and scenes Next The main grid electricity sales of a single island microgrid.

8. The method according to claim 3, characterized in that The second objective function is constructed according to the following formula, including: In the formula, is the second objective function, For the scene Probability of occurrence; To adjust the flexibility resources of the island microgrid cluster at its operation point at time 𝑡; is the power change of the flexible power unit of the island microgrid 𝑘 under time 𝑡 and scenario 𝑠; The load reduction resources for the island microgrid 𝑘; is the upper adjustment load reduction under time 𝑡 and scenario 𝑠; The price of electricity purchased from the main grid at time 𝑡; is the amount of electricity purchased by the 𝑘th island microgrid from the main grid at time 𝑡 and scenario 𝑠; is the price of electricity sold to the main grid at time 𝑡; is the amount of electricity sold by the 𝑘th island microgrid to the main grid at time 𝑡 and scenario 𝑠; is the discharge resource of MGC energy storage unit 𝑖; is the discharge amount of MGC energy storage unit 𝑖 at time 𝑡 and scenario 𝑠.

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