Distribution network scheduling method, device, storage medium and electronic device including multiple microgrids

Abstract

The present application provides a distribution network scheduling method, device, storage medium and electronic device containing a multi-dimensional network; the method includes: constructing a distribution network optimization objective function for the distribution network, and constructing a first constraint condition for the distribution network optimization objective function; constructing a virtual power plant optimization objective function for the virtual power plant, and constructing a second constraint condition for the virtual power plant optimization objective function; constructing a respective microgrid optimization objective function for the consumption data of each microgrid as a target, and constructing a corresponding third constraint condition; decoupling the distribution network optimization objective function, the virtual power plant optimization objective function and each microgrid optimization objective function into a first objective function, a second objective function and a third objective function, and optimizing the first objective function, the second objective function and each third objective function iteratively until the convergence condition is met and the iteration is stopped, and power scheduling is performed based on the current first objective function, the second objective function and each third objective function.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of power dispatching, and in particular to a method, device, storage medium, and electronic device for dispatching a distribution network containing multiple microgrids. Background Art

[0002] The integration of distributed renewable energy sources such as photovoltaic and wind power increases the uncertainty of the power system, especially its randomness and intermittent characteristics, making it very difficult to dispatch and operate traditional power systems. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a distribution network scheduling method, device, storage medium and electronic equipment containing multiple microgrids.

[0004] Based on the above objectives, the present application provides a distribution network scheduling method containing multiple microgrids, which is applied to a distribution network that is connected to wind turbines, gas turbines, energy storage devices and virtual power plants, and distributes power to multiple microgrids;

[0005] The method includes:

[0006] With the goal of maximizing the return data of the distribution network, a distribution network optimization objective function is constructed, and a first constraint condition is constructed for the distribution network optimization objective function based on the output of the wind turbine, the output of the gas turbine, and the capacity of the energy storage device;

[0007] With the goal of maximizing the return data of the virtual power plant, a virtual power plant optimization objective function is constructed, and a second constraint condition is constructed for the virtual power plant optimization objective function according to the operating state of the gas turbine and the operating state of the energy storage device;

[0008] For each microgrid, with the goal of minimizing the consumption data of the microgrid, a respective microgrid optimization objective function is constructed for each microgrid, and a respective corresponding third constraint condition is constructed for each microgrid optimization objective function;

[0009] The distribution network optimization objective function, the virtual power plant optimization objective function and the microgrid optimization objective function are decoupled into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant and a third objective function corresponding to each microgrid. A convergence condition is constructed using a preset convergence threshold, and the first objective function, the second objective function and the third objective function are optimized iteratively until the convergence condition is met and the iteration is stopped. Power scheduling is performed using the current first objective function, the second objective function and the third objective function.

[0010] Furthermore, with the goal of maximizing the return data of the distribution network, a distribution network optimization objective function is constructed, including:

[0011] With the goal of maximizing the return data of the distribution network, the distribution network optimization objective function is constructed as follows:

[0012]

[0013] Wherein, maxL1 represents the maximum return data of the distribution network, N represents the number of control scenarios when the distribution network distributes power, T represents the number of scenario periods when the distribution network distributes power, ω n represents the probability of scenario n occurring when the distribution network distributes power, represents the first partial return data of the distribution network's peak load regulation on the previous day, representing second local reporting data of the generator sets of the power distribution network, a third local reporting data indicating the frequency regulation reserve capacity of the distribution network, represents the first local consumption data of the distribution network's peak load regulation the previous day, representing second local consumption data representing a generator set of said power distribution network, The third local consumption data represents the frequency regulation reserve capacity of the power distribution network.

[0014] Furthermore, a first constraint condition is constructed for the distribution network optimization objective function based on the output of the wind turbine, the output of the gas turbine, and the capacity of the energy storage device, including:

[0015] Determine the maximum output of the wind turbine generator set according to the day-ahead predicted wind speed, divide the output into the internal consumption of the wind turbine generator set and the planned wind power transmission, and determine the wind power frequency regulation reserve capacity when the frequency is oriented downward within the planned transmission range;

[0016] Constructing a first wind power constraint condition on the wind turbine generator set by using the internal absorption capacity and the wind power frequency regulation reserve capacity;

[0017] dividing the output of the gas turbine into internal supply of the gas turbine, planned external power supply of the gas turbine, and conventional reserve capacity of the gas turbine, and determining upward frequency regulation reserve capacity and downward frequency regulation reserve capacity of the gas turbine based on the external power supply;

[0018] A first gas turbine constraint condition is constructed for the gas turbine using the internal supply of the gas turbine, the planned external power transmission of the gas turbine, the conventional spare capacity of the gas turbine, the upward frequency regulation spare capacity of the gas turbine, and the downward frequency regulation spare capacity of the gas turbine.

[0019] Furthermore, with the goal of maximizing the return data of the virtual power plant, a virtual power plant optimization objective function is constructed, including:

[0020] Taking the maximization of the return data of the virtual power plant as the goal, the distribution network optimization objective function is constructed as follows:

[0021]

[0022] Wherein, maxL2 represents the maximum return data of the virtual power plant, represents the amount of electricity obtained by the distribution network from the mth distributed resource cluster during period t, represents the frequency regulation capacity obtained by the distribution network from the mth distributed resource cluster during period t, represents the frequency regulation capacity obtained by the distribution network from the mth distributed resource cluster during period t, Represents the operating consumption data within the mth distributed resource cluster during time period t.

[0023] Furthermore, a second constraint condition is constructed for the virtual power plant optimization objective function according to the operating state of the gas turbine and the operating state of the energy storage device, including:

[0024] constructing a second gas turbine constraint condition for the gas turbine by using the maximum power, minimum power, upper frequency regulation capacity and lower frequency regulation capacity of the gas turbine during operation of the gas turbine;

[0025] The charging power, discharging power, energy storage up-frequency regulation capacity and energy storage down-frequency regulation capacity of the energy storage device are used to construct a second energy storage constraint condition for the energy storage device.

[0026] Further, the plurality of microgrids include an industrial microgrid, a residential microgrid, and a commercial microgrid;

[0027] Each microgrid constructs its own microgrid optimization objective function, and constructs its own corresponding third constraint condition for each microgrid optimization objective function, including:

[0028] Taking minimization of consumption data of the industrial microgrid as an optimization goal, constructing a third industrial objective function for the industrial microgrid, and constructing a third industrial constraint condition for the third industrial objective function according to the operation logic, operation time, task volume and storage condition of the industrial microgrid;

[0029] Taking minimization of consumption data of the residential microgrid as an optimization goal, constructing a third resident objective function for the residential microgrid, and constructing a third resident constraint condition for the third resident objective function according to the power load of the residential microgrid;

[0030] Taking minimization of the consumption data of the commercial microgrid as the optimization goal, a third business objective function for the commercial microgrid is constructed, and a third business constraint condition is constructed for the third business objective function based on the cooling energy and electric energy of the commercial microgrid.

[0031] Furthermore, the distribution network optimization objective function, the virtual power plant optimization objective function, and each microgrid optimization objective function are decoupled into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant, and a third objective function corresponding to each microgrid, including:

[0032] By using a first interconnected variable preset for the distribution network, a second interconnected variable preset for the virtual power plant, and a third interconnected variable preset for each microgrid, the distribution network optimization objective function, the virtual power plant optimization objective function, and each microgrid optimization objective function are decoupled to obtain a first objective function corresponding to the distribution network optimization objective function as shown below:

[0033]

[0034] The second objective function corresponding to the virtual power plant optimization objective function,

[0035]

[0036] The third industrial decoupling objective function corresponding to the third industrial objective function,

[0037]

[0038] A third resident decoupling objective function corresponding to the third resident objective function,

[0039]

[0040] A third business decoupling objective function corresponding to the third business objective function,

[0041]

[0042] Among them, K DN represents the first interconnected variable, K DR represents the second interconnected variable, nK WW represents n third interconnected variables, V t represents the first multiplication factor of time period t, W t represents the second multiplication factor of time period t;

[0043] The first multiplier and the second multiplier satisfy the following formula:

[0044]

[0045] W t，k+1 =πW t，k

[0046] Here, k represents the kth iteration.

[0047] Based on the same inventive concept, the present application also provides a distribution network dispatching device containing multiple microgrids, comprising: a first optimization module, a second optimization module, a third optimization module and an iteration module;

[0048] The first optimization module is configured to construct a distribution network optimization objective function with the goal of maximizing the return data of the distribution network, and to construct a first constraint condition for the distribution network optimization objective function based on the output of the wind turbine, the output of the gas turbine, and the capacity of the energy storage device;

[0049] The second optimization module is configured to construct a virtual power plant optimization objective function with the goal of maximizing the return data of the virtual power plant, and to construct a second constraint condition for the virtual power plant optimization objective function according to the operating state of the gas turbine and the operating state of the energy storage device;

[0050] The optimization module is configured to, for each microgrid, construct a respective microgrid optimization objective function for each microgrid with the goal of minimizing the consumption data of the microgrid, and construct a respective corresponding third constraint condition for each microgrid optimization objective function;

[0051] The iteration module is configured to decouple the distribution network optimization objective function, the virtual power plant optimization objective function and the optimization objective function of each microgrid into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant and a third objective function corresponding to each microgrid, construct a convergence condition using a preset convergence threshold, and optimize and iterate the first objective function, the second objective function and each third objective function until the convergence condition is met and the iteration is stopped, and power scheduling is performed based on the current first objective function, the second objective function and each third objective function.

[0052] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the distribution network scheduling method containing multiple microgrids as described in any one of the above items.

[0053] Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned distribution network scheduling method containing multiple microgrids.

[0054] From the above, it can be seen that the distribution network scheduling method, device, storage medium and electronic device containing multiple microgrids provided in this application are based on the virtual power plant in the distribution network and the various microgrids connected to the distribution network. The wind turbines, gas turbines and energy storage devices that connect electricity to the distribution network are comprehensively considered to construct the distribution network optimization objective function and its constraints for the input electricity in the distribution network. At the same time, the operation of the gas turbines and energy storage devices are comprehensively considered to construct the virtual power plant optimization objective function and its constraints for the virtual power plant, and the consumption of each microgrid is further considered to construct the respective microgrid optimization objective function and its corresponding constraints for each microgrid. Based on this, the objective function that meets the convergence conditions can be determined by iterative calculation of each objective function, and the power scheduling of the distribution network can be carried out accordingly. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 This is a flow chart of a method for dispatching a distribution network containing multiple microgrids according to an embodiment of the present application;

[0057] Figure 2 This is a schematic structural diagram of a distribution network dispatching device containing multiple microgrids according to an embodiment of the present application;

[0058] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0060] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0061] As described in the background technology section, the related distribution network dispatching method containing multiple microgrids is still difficult to meet the needs of the distribution network in actual power dispatching.

[0062] In the process of implementing this application, the applicant found that the main problem with the relevant distribution network scheduling method containing multiple microgrids is that the access of distributed new energy sources such as photovoltaic and wind power increases the uncertainty of the power system, especially its randomness and intermittent characteristics, making it very difficult to schedule and operate traditional power systems.

[0063] Traditional centralized scheduling methods have problems such as huge data volume, difficult maintenance and privacy leakage when facing distributed resources that are large in number, small in capacity and geographically dispersed.

[0064] Based on this, one or more embodiments of the present application provide a distribution network scheduling method containing multiple microgrids.

[0065] In the embodiments of the present application, application scenarios include distribution networks, wind turbines, gas turbines, energy storage equipment, virtual power plants, and multiple microgrids.

[0066] Among them, the distribution network is connected to multiple wind turbines, multiple gas turbines and multiple energy storage devices, and receives electricity from the wind turbines, gas turbines and energy storage devices. In addition, the distribution network is also connected to multiple microgrids and distributes electricity to each microgrid. Moreover, the distribution network is also connected to a virtual power plant for controlling power distribution.

[0067] In this embodiment, the microgrid specifically includes an industrial microgrid, a residential microgrid, and a commercial microgrid.

[0068] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0069] refer to Figure 1A distribution network scheduling method including multiple microgrids according to an embodiment of the present application is applied to a distribution network that is connected to a wind turbine, a gas turbine, an energy storage device, and a virtual power plant, and distributes power to multiple microgrids; and specifically includes the following steps:

[0070] Step S101: constructing a distribution network optimization objective function with the goal of maximizing the return data of the distribution network, and constructing a first constraint condition for the distribution network optimization objective function based on the output of the wind turbine, the output of the gas turbine and the capacity of the energy storage device.

[0071] In an embodiment of the present application, when performing power dispatch, the distribution network will generate returns and consumption. Based on this, an objective function for the distribution network can be constructed by maximizing the return data of the distribution network, and corresponding constraints can be constructed for the objective function.

[0072] Specifically, the maximization of the return data of the distribution network can be used as the optimization goal to construct the distribution network optimization objective function as shown below:

[0073]

[0074] Among them, maxL1 represents the maximum return data of the distribution network, N represents the number of control scenarios when the distribution network distributes power, T represents the number of scenario periods when the distribution network distributes power, ω n represents the probability of scenario n occurring when the distribution network distributes power, Indicates the first partial return data of the distribution network's peak load regulation. The second local reporting data of the generator set of the distribution network, The third local report data representing the frequency regulation reserve capacity of the distribution network, Indicates the first local consumption data of the distribution network's peak load regulation. representing second local consumption data of a generator set of the power distribution network, The third local consumption data represents the frequency regulation reserve capacity of the distribution network.

[0075] Furthermore, for and They can be calculated according to the following formulas:

[0076]

[0077] in, represents the gas purchase price of the distribution network, represents the day-ahead electricity price, π p represents the market peak-shaving electricity price, represents the clearing price on the power generation side, represents the clearing price of frequency regulation reserve capacity, βres represents the opportunity cost loss coefficient, β co Indicates the equivalent coupling coefficient of heat load participating in the peak load regulation market, β h Indicates the equivalent coupling coefficient of cooling load participating in the peak load regulation market, The fourth local consumption data of the gas turbine operation, E i represents the fifth local consumption data of the i-th device in the set J of other energy devices in the distribution network; represents the day-ahead gas purchase curve, represents the electricity purchase curve, represents the conventional backup plan curve, Represents the output curve of energy equipment i in the distribution network, It represents the electrical peak regulation response curve. represents the cold peak response curve, represents the thermal peak regulation response curve, represents the power generation transmission curve of the distribution network, represents the upward frequency regulation reserve capacity curve of the distribution network, Represents the downward frequency regulation reserve capacity curve of the distribution network.

[0078] Furthermore, based on the determined distribution network optimization objective function and the calculation formulas of the above-mentioned parameters, a first constraint condition can be constructed for it.

[0079] In this embodiment, the wind turbine generator set, gas turbine and energy storage device can be divided into respective first constraint conditions to establish their own.

[0080] Specifically, for wind turbines, the day-ahead wind speed can be predicted first. After obtaining the day-ahead predicted wind speed, the maximum output under the day-ahead predicted wind speed can be determined and divided into the internal wind power consumption and the planned wind power transmission.

[0081] Furthermore, the above-mentioned internal absorption capacity and wind power frequency regulation reserve capacity can be used to construct the first wind power constraint condition of the distribution network optimization objective function as shown below to constrain the operation of wind turbines:

[0082]

[0083] Among them, among them, It indicates the maximum output of the wind turbine according to the wind speed predicted on the previous day. express Internal consumption of wind turbines, express Frequency regulation reserve capacity of wind turbines when frequency regulation is oriented downward within the scope of unplanned power transmission. Indicates the minimum proportion of wind power transmitted by wind turbines. Indicates the maximum proportion of wind power transmitted by wind turbines. Indicates the maximum proportion of wind turbine frequency regulation reserve capacity.

[0084] Furthermore, for gas turbines, their processing can be divided into gas turbine internal supply, gas turbine planned external power supply and gas turbine conventional spare capacity.

[0085] Based on this, the upward frequency regulation reserve capacity and the downward frequency regulation reserve capacity of the gas turbine can be determined based on the range of the planned power transmission of the gas turbine.

[0086] Furthermore, using the aforementioned gas turbine internal supply capacity, gas turbine planned external power transmission, gas turbine conventional reserve capacity, gas turbine upward frequency regulation reserve capacity, and gas turbine downward frequency regulation reserve capacity, the following first gas turbine constraint condition for the distribution network optimization objective function can be constructed:

[0087]

[0088] in, represents the internal supply volume of the gas turbine, Indicates the planned outbound power of the gas turbine. represents the conventional spare capacity of the gas turbine, Indicates the gas turbine upward frequency regulation reserve capacity, represents the reserve capacity for downward frequency regulation of the gas turbine, S gt Indicates the rated capacity of the gas turbine, represents the start and stop variable of the gas turbine on the day before, and its value is between 0 and 1. gt represents the hourly ramp rate of the gas turbine, Indicates the maximum ratio of the gas turbine's downward frequency regulation capacity, Indicates the minimum ratio of gas turbine upward frequency regulation capacity, Indicates the maximum ratio of gas turbine upward frequency regulation capacity, Indicates the minimum ratio of conventional reserve capacity of gas turbine, Δ gt Indicates the duration of the gas turbine's early activation of the reserve capacity, which can be 4 hours. represents the start-stop consumption of the gas turbine, Represents the operating consumption of the gas turbine.

[0089] Furthermore, for the energy storage device, the upward frequency regulation reserve capacity and the downward frequency regulation reserve capacity of the energy storage device can be determined, and based on this, the first energy storage constraint condition for the distribution network optimization objective function as shown below is constructed:

[0090]

[0091] in, Indicates the upward frequency regulation reserve capacity of the energy storage device. Indicates the reserve capacity of energy storage equipment for downward frequency regulation. Indicates the conventional backup capacity of energy storage equipment, It represents the minimum ratio of conventional backup capacity of energy storage equipment, and Δt represents the time interval.

[0092] Step S102: With the goal of maximizing the return data of the virtual power plant, construct a virtual power plant optimization objective function, and construct a second constraint condition for the virtual power plant optimization objective function based on the operating state of the gas turbine and the operating state of the energy storage device.

[0093] In an embodiment of the present application, when conducting power dispatch, the virtual power plant will also generate returns and consumption. Based on this, the objective function of the virtual power plant can be constructed by maximizing the return data of the virtual power plant, and corresponding constraints can be constructed for the objective function.

[0094] Specifically, the maximization of the return data of the virtual power plant can be used as the optimization goal to construct the virtual power plant optimization objective function as shown below:

[0095]

[0096] Among them, maxL2 represents the maximum return data of the virtual power plant, represents the amount of electricity obtained by the distribution network from the mth distributed resource cluster during period t, represents the frequency regulation capacity obtained by the distribution network from the mth distributed resource cluster during period t, represents the frequency regulation capacity obtained by the distribution network from the mth distributed resource cluster during period t, Represents the operating consumption data within the mth distributed resource cluster during time period t.

[0097] Furthermore, based on the determined virtual power plant optimization objective function, a second constraint condition can be constructed for it.

[0098] In this embodiment, the second constraint conditions can be constructed for wind turbines, gas turbines and energy storage devices respectively to constrain the operation of the virtual power plant.

[0099] Specifically, for each gas turbine, the maximum power and minimum power of each gas turbine when currently running can be determined first, and based on this, the gas turbine up-regulation capacity and the gas turbine down-regulation capacity of each gas turbine can be determined.

[0100] Based on this, the second gas turbine constraint condition for the gas turbine in the virtual power plant optimization objective function can be constructed as follows:

[0101]

[0102] in, represents the maximum power of the kth gas turbine during operation in period t, represents the minimum power of the kth gas turbine during operation in period t, represents the frequency regulation capacity of the kth gas turbine in period t, represents the frequency regulation capacity of the kth gas turbine in period t, represents the hourly ramp rate of the kth gas turbine, represents the maximum proportion of the frequency regulation capacity of the kth gas turbine, represents the minimum proportion of frequency regulation capacity on the kth gas turbine, It represents the maximum proportion of frequency regulation capacity on the kth gas turbine.

[0103] Furthermore, for each energy storage device, the charge and discharge power of each energy storage device in the current period can be determined first, and based on this, the upper frequency regulation capacity and the lower frequency regulation capacity of the energy storage device can be determined.

[0104] Based on this, the second energy storage constraint condition for energy storage equipment in the virtual power plant optimization objective function can be constructed as follows:

[0105]

[0106] in, represents the charging power of the mth energy storage device in time period t, represents the maximum charge and discharge power of the mth energy storage device, represents the frequency regulation capacity provided by the mth energy storage device in time period t, represents the frequency regulation capacity provided by the mth energy storage device in time period t, represents the state of charge of the mth energy storage device in time period t, η c represents the charging efficiency of the energy storage device, η d represents the discharge efficiency of the energy storage device, E represents the capacity of the energy storage device, and Δt represents the time interval during scheduling. For example, it can be 1 hour in day-ahead scheduling.

[0107] Step S103: For each microgrid, with the goal of minimizing the consumption data of the microgrid, construct a respective microgrid optimization objective function for each microgrid, and construct a respective corresponding third constraint condition for each microgrid optimization objective function.

[0108] In an embodiment of the present application, when power scheduling is performed, each microgrid connected to the distribution network will generate consumption. Based on this, for each microgrid, an objective function for the microgrid can be constructed by minimizing the consumption data of the microgrid, and corresponding constraints can be constructed for the objective function.

[0109] Specifically, for industrial microgrids, residential microgrids and commercial microgrids, respective objective functions and corresponding constraints can be constructed.

[0110] Among them, for industrial microgrids, the industrial microgrid's electricity consumption when the industrial microgrid is connected to electricity from the distribution network can be determined, as well as the industrial microgrid's gas consumption when the industrial microgrid is connected to gas from other gas distribution systems that provide gas.

[0111] Furthermore, the minimization of the consumption data of the industrial microgrid is taken as the optimization goal to construct the third industrial objective function as shown below:

[0112]

[0113] Among them, G IM，t represents the electricity purchase price of the industrial microgrid, P IM，t Indicates the purchased power of industrial microgrid, G gas represents the gas purchase price of the industrial microgrid, E gas，t Indicates the gas purchase volume of the industrial microgrid.

[0114] Based on this, for industrial microgrids, we can first determine their operating logic, operating time, task volume and storage conditions, and construct their corresponding constraints respectively.

[0115] Specifically, the operation logic of the industrial microgrid may include the constraint relationship between the operation variables, start variables, and stop variables of the industrial microgrid. Based on this, the following industrial microgrid operation logic constraint conditions can be constructed:

[0116] mv pl，t -av pl，t =bv pl，t -bv pl，t-1

[0117] mv pl，t +av pl，t ≤1

[0118] Among them, mv pl , t represents the startup variable of any packaging line in the industrial microgrid, av pl，t represents the stopping variable of the packaging line in the industrial microgrid, bv pl，t Represents the operating variables of the packaging line in the industrial microgrid.

[0119] Furthermore, for the operating time of the industrial microgrid, the operating time constraints of the industrial microgrid can be constructed according to its maximum operating time and minimum operating time as shown below:

[0120]

[0121] Among them, T pl，on，min Indicates the minimum operating time of the packaging line, T pl，on，max It represents the maximum running time of the packaging line, and h represents any time in the scheduling period.

[0122] Furthermore, regarding the task volume of the industrial microgrid, according to the task packaging process, the following industrial microgrid operating time constraints can be constructed:

[0123]

[0124] Among them, N pl Indicates the total number of baling lines, p indicates the baling line number (p=1,2,...,N pl ), R pl Represents the production per unit time of any single packaging line, R pl，sum Indicates the total amount of tasks in the packaging process.

[0125] Furthermore, for storage conditions, the following storage constraints are constructed:

[0126]

[0127] Among them, s cw，min Indicates the maximum storage capacity of the warehouse between the production line and the packaging line of electric energy, s cw，max Indicates the minimum storage capacity of the warehouse between the production line and the packaging line of electric energy, s pw，min Indicates the minimum value of the warehouse storage capacity between the packaging line and the aging line of electrical energy, s pw，max Indicates the maximum amount of storage capacity in the warehouse between the packaging line and the aging line of electrical energy.

[0128] In this embodiment, for the residential microgrid, the electricity purchase consumption of the residential microgrid when the residential microgrid is connected to the power distribution network can be determined.

[0129] Furthermore, the minimization of the consumption data of the residential microgrid is taken as the optimization goal to construct the third resident objective function as shown below:

[0130]

[0131] Among them, M RM，t represents the electricity purchase price of the residential microgrid, P RM，tRepresents the purchased electricity power of the residential microgrid.

[0132] Based on this, for residential microgrids, we can first determine their various types of power loads and construct their corresponding constraints respectively.

[0133] Specifically, various types of electricity consumption may include, for example, residential air-conditioning electricity consumption, residential cooling load, and residential electricity load.

[0134] For the residential air conditioning power load in the residential microgrid, the following air conditioning power consumption constraint conditions can be constructed:

[0135]

[0136] Among them, P R，ae，min Indicates the minimum value of residential air conditioning output, P R，ae，max Indicates the maximum value of residential air conditioning output, H R，ae，t Indicates the cooling power of residential air conditioner; P R，ae，t Indicates the power consumption of residential air conditioning; η ae，R Indicates the air conditioning efficiency of residents.

[0137] Furthermore, for the residential cooling load in the residential microgrid, the following residential cooling energy balance constraint conditions can be constructed:

[0138] Q R，t =η ae，R P R，ae，t

[0139] Among them, Q R，t Represents the cooling load in the residential microgrid.

[0140] Furthermore, for the residential power in the residential microgrid, the following residential power constraint conditions can be constructed:

[0141] P RM，t +P R，PV，t =P R,ae,t +P Rload，t

[0142] Among them, P RM，t Indicates the electricity purchased by residents; P Rload，t Represents other residential fixed electrical loads in the residential microgrid.

[0143] In this embodiment, for a commercial microgrid, the electricity purchase consumption of the commercial microgrid when the commercial microgrid accesses electricity from the power distribution network can be determined.

[0144] Furthermore, minimizing the consumption data of the commercial microgrid is taken as the optimization goal to construct the third commercial objective function as shown below:

[0145]

[0146] Among them, M CM，t Represents the electricity purchase price of the commercial microgrid.

[0147] Based on this, for commercial microgrids, we can first determine their various types of power loads and construct their corresponding constraints respectively.

[0148] Specifically, various types of electricity loads may include, for example, commercial cooling loads and commercial electricity loads.

[0149] Furthermore, for the commercial cooling load in the commercial microgrid, the following commercial cooling energy balance constraint conditions can be constructed:

[0150] Q C,t =η ae,C P C,ae，t

[0151] Among them, Q R，t Represents the cooling load within the commercial microgrid.

[0152] Furthermore, for commercial power in a commercial microgrid, the following commercial power constraints can be constructed:

[0153] P CM，t +P C，PV，t =P C，ae，t +P Cload，t

[0154] Among them, P CM，t Represents commercial power purchase; P Cload，t Represents other commercial fixed electric loads in the commercial microgrid.

[0155] Step S104: decouple the distribution network optimization objective function, the virtual power plant optimization objective function and the microgrid optimization objective function into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant and a third objective function corresponding to each microgrid, construct a convergence condition using a preset convergence threshold, and perform optimization iterations on the first objective function, the second objective function and the third objective functions until the convergence condition is met and the iteration is stopped, and power scheduling is performed using the current first objective function, the second objective function and the third objective functions.

[0156] In an embodiment of the present application, based on the distribution network optimization objective function, the virtual power plant optimization objective function, the third industrial objective function for the industrial microgrid, the third residential objective function for the residential microgrid, and the third commercial objective function for the commercial microgrid determined in the aforementioned steps, they can be decoupled and iteratively calculated to determine a power dispatching plan that is suitable for the distribution network, virtual power plant and each microgrid.

[0157] Specifically, due to the high coupling between the distribution network, virtual power plant and each microgrid, it is impossible to determine the optimal solution that adapts to the distribution network, virtual power plant and each microgrid by directly and independently solving each optimization objective function. Therefore, in response to the above problem, the target cascade method is used in this embodiment to decouple each optimization objective function separately.

[0158] Furthermore, after decoupling, the following optimization objective functions are obtained:

[0159] The first objective function corresponding to the distribution network optimization objective function is:

[0160] The second objective function corresponding to the virtual power plant optimization objective function is:

[0161] The third industry decoupling objective function corresponding to the third industry objective function:

[0162] The third resident decoupling objective function corresponding to the third resident objective function:

[0163] The third business decoupling objective function corresponding to the third business objective function:

[0164] Among them, K DN Represents the first interconnected variable, K DR Represents the second interconnected variable, nK WW Represents n third interconnected variables, V t represents the first multiplication factor of time period t, W t represents the second multiplier of time period t.

[0165] During the iterative operation, the first multiplier and the second multiplier must satisfy the following relationship:

[0166]

[0167] W t，k+1 =πW t，k

[0168] Here, k represents the kth iteration and π represents a constant.

[0169] Based on this, the above-mentioned first objective function, second objective function, third industrial decoupling objective function, third resident decoupling objective function and third commercial decoupling objective function can be used to perform iterative operations, and construct convergence conditions for determining whether the iterative operations should be stopped.

[0170] Specifically, the iterative convergence condition can be constructed as follows:

[0171]

[0172] in, represents the first interconnected variable at the kth iteration, represents the second interconnected variable at the kth iteration, Represents the third interconnected variable at the k-th iteration.

[0173] Furthermore, we can As the overall economic model of each objective function, ε represents the convergence parameter of the overall return of each objective function, and by adding 1 to the denominator, it is possible to avoid the denominator being close to 0.

[0174] Based on this, before the first iteration of the iterative operation, the initial values of the first interconnected variable, the second interconnected variable and the third interconnected variable can be set respectively, and the number of iterations k is set to 0, thereby further determining the first multiplier and the second multiplier, and the first iteration can be performed.

[0175] Specifically, in each iteration, the first objective function of the distribution network may be optimized first, and the first interconnected variable of this iteration may be determined by combining various objective functions for calculation.

[0176] Furthermore, the first interconnected variable obtained in this round of iteration, as well as the second interconnected variable and the third interconnected variable obtained in the previous round of iteration, are used to optimize the second objective function of the virtual power plant. By combining the various objective functions for calculation, the second interconnected variable of this round of iteration is determined.

[0177] Furthermore, the first interconnected variable obtained in this round of iteration, the second interconnected variable obtained in this round of iteration, and the third interconnected variable obtained in the previous round of iteration are used to optimize the third industrial decoupling objective function, the third resident decoupling objective function and the third commercial decoupling objective function. By combining the various objective functions for calculation, the third interconnected variable of this round of iteration is determined.

[0178] Based on this, the first interconnected variable, the second interconnected variable, and the third interconnected variable of this round of iteration are used to determine whether the convergence condition is satisfied in the current iteration.

[0179] Furthermore, if the convergence condition is not met, the next round of iteration is performed until the convergence condition is met, and the iteration is stopped; if the convergence condition is met, the various objective functions output in the current round are used, and the distribution network is instructed to perform power dispatch according to the various objective functions determined in the current round.

[0180] It can be seen that the distribution network scheduling method containing multiple microgrids in the embodiment of the present application is based on the virtual power plant in the distribution network and the various microgrids connected to the distribution network. It comprehensively considers the wind turbines, gas turbines and energy storage equipment that connect electricity to the distribution network to construct the distribution network optimization objective function and its constraints for the input electricity in the distribution network. At the same time, it comprehensively considers the operation of the gas turbines and energy storage equipment to construct the virtual power plant optimization objective function and its constraints for the virtual power plant, and further considers the consumption of each microgrid to construct a respective microgrid optimization objective function and its corresponding constraints for each microgrid. Based on this, the objective function that meets the convergence conditions can be determined by iterative calculation of each objective function, and the power scheduling of the distribution network can be carried out accordingly.

[0181] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices working together. In the case of such a distributed scenario, one of the multiple devices may only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method described.

[0182] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0183] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, an embodiment of the present application also provides a distribution network dispatching device containing multiple microgrids.

[0184] refer to Figure 2 , the distribution network dispatching device containing multiple microgrids includes: a first optimization module 201, a second optimization module 202, a third optimization module 203 and an iteration module 204;

[0185] The first optimization module 201 is configured to construct a distribution network optimization objective function with the goal of maximizing the return data of the distribution network, and to construct a first constraint condition for the distribution network optimization objective function based on the output of the wind turbine, the output of the gas turbine, and the capacity of the energy storage device;

[0186] The second optimization module 202 is configured to construct a virtual power plant optimization objective function with the goal of maximizing the return data of the virtual power plant, and to construct a second constraint condition for the virtual power plant optimization objective function according to the operating state of the gas turbine and the operating state of the energy storage device;

[0187] The optimization module 203 is configured to, for each microgrid, construct a respective microgrid optimization objective function for each microgrid with the goal of minimizing the consumption data of the microgrid, and construct a respective corresponding third constraint condition for each microgrid optimization objective function;

[0188] The iteration module 204 is configured to decouple the distribution network optimization objective function, the virtual power plant optimization objective function and the optimization objective function of each microgrid into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant and a third objective function corresponding to each microgrid, construct a convergence condition using a preset convergence threshold, and optimize and iterate the first objective function, the second objective function and each third objective function until the convergence condition is met and the iteration is stopped, and power scheduling is performed based on the current first objective function, the second objective function and each third objective function.

[0189] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0190] The device of the above embodiment is used to implement the corresponding distribution network scheduling method containing multiple microgrids in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0191] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the distribution network scheduling method containing multiple microgrids as described in any of the above embodiments.

[0192] Figure 310 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0193] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0194] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of the present application are implemented through software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010.

[0195] The input / output interface 1030 is used to connect an input / output module to implement information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0196] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0197] The bus 1050 comprises a path for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0198] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of the present application, and does not necessarily include all the components shown in the figure.

[0199] The device of the above embodiment is used to implement the corresponding distribution network scheduling method containing multiple microgrids in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0200] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the distribution network scheduling method containing multiple microgrids as described in any of the above embodiments.

[0201] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0202] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the distribution network scheduling method containing multiple microgrids as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0203] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0204] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the following fact, that is, the details of the implementation of these block diagram devices are highly dependent on the platform of the embodiment to be implemented in the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0205] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0206] The embodiments of the present application are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for dispatching a distribution network containing multiple microgrids, characterized in that: Applied to distribution networks that connect to wind turbines, gas turbines, energy storage devices, and virtual power plants, and distribute power to multiple microgrids; The method comprises: With the goal of maximizing the return data of the distribution network, a distribution network optimization objective function is constructed, and a first constraint condition is constructed for the distribution network optimization objective function based on the output of the wind turbine, the output of the gas turbine, and the capacity of the energy storage device; wherein, constructing the distribution network optimization objective function with the goal of maximizing the return data of the distribution network includes: With the goal of maximizing the return data of the distribution network, the distribution network optimization objective function is constructed as follows: Wherein, maxL1 represents the maximum return data of the distribution network, N represents the number of control scenarios when the distribution network distributes power, T represents the number of scenario periods when the distribution network distributes power, ω n represents the probability of scenario n occurring when the distribution network distributes power, represents the first partial return data of the distribution network's peak load regulation on the previous day, representing second local reporting data of the generator sets of the power distribution network, a third local reporting data indicating the frequency regulation reserve capacity of the distribution network, represents the first local consumption data of the distribution network's peak load regulation the day before, representing second local consumption data representing a generator set of said power distribution network, third local consumption data representing a frequency regulation reserve capacity of the distribution network; With the goal of maximizing the return data of the virtual power plant, a virtual power plant optimization objective function is constructed, and a second constraint condition is constructed for the virtual power plant optimization objective function according to the operating state of the gas turbine and the operating state of the energy storage device; For each microgrid, with the goal of minimizing the consumption data of the microgrid, a respective microgrid optimization objective function is constructed for each microgrid, and a respective corresponding third constraint condition is constructed for each microgrid optimization objective function; The distribution network optimization objective function, the virtual power plant optimization objective function and the microgrid optimization objective function are decoupled into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant and a third objective function corresponding to each microgrid. A convergence condition is constructed using a preset convergence threshold, and the first objective function, the second objective function and the third objective function are optimized iteratively until the convergence condition is met and the iteration is stopped. Power scheduling is performed using the current first objective function, the second objective function and the third objective function.

2. The method according to claim 1, characterized in that The constructing of a first constraint condition for the distribution network optimization objective function based on the output of the wind turbine generator set, the output of the gas turbine, and the capacity of the energy storage device includes: Determine the maximum output of the wind turbine generator set according to the day-ahead predicted wind speed, divide the output into the internal consumption of the wind turbine generator set and the planned wind power transmission, and determine the wind power frequency regulation reserve capacity when the frequency is oriented downward within the planned transmission range; Constructing a first wind power constraint condition on the wind turbine generator set by using the internal absorption capacity and the wind power frequency regulation reserve capacity; dividing the output of the gas turbine into internal supply of the gas turbine, planned external power supply of the gas turbine, and conventional reserve capacity of the gas turbine, and determining upward frequency regulation reserve capacity and downward frequency regulation reserve capacity of the gas turbine based on the external power supply; A first gas turbine constraint condition is constructed for the gas turbine using the internal supply of the gas turbine, the planned external power transmission of the gas turbine, the conventional spare capacity of the gas turbine, the upward frequency regulation spare capacity of the gas turbine, and the downward frequency regulation spare capacity of the gas turbine.

3. The method according to claim 1, characterized in that The virtual power plant optimization objective function is constructed with the goal of maximizing the return data of the virtual power plant, including: Taking the maximization of the return data of the virtual power plant as the goal, the distribution network optimization objective function is constructed as follows: Wherein, maxL2 represents the maximum return data of the virtual power plant, represents the amount of electricity obtained by the distribution network from the mth distributed resource cluster during period t, represents the frequency regulation capacity obtained by the distribution network from the mth distributed resource cluster during period t, represents the frequency regulation capacity obtained by the distribution network from the mth distributed resource cluster during period t, Represents the operating consumption data within the mth distributed resource cluster during time period t.

4. The method according to claim 1, wherein The constructing of a second constraint condition for the virtual power plant optimization objective function according to the operating state of the gas turbine and the operating state of the energy storage device includes: constructing a second gas turbine constraint condition for the gas turbine by using the maximum power, minimum power, upper frequency regulation capacity and lower frequency regulation capacity of the gas turbine during operation of the gas turbine; The charging power, discharging power, energy storage up-frequency regulation capacity and energy storage down-frequency regulation capacity of the energy storage device are used to construct a second energy storage constraint condition for the energy storage device.

5. The method according to claim 1, wherein The plurality of microgrids include industrial microgrids, residential microgrids and commercial microgrids; Each microgrid constructs its own microgrid optimization objective function, and constructs a corresponding third constraint condition for each microgrid optimization objective function, including: Taking minimization of consumption data of the industrial microgrid as an optimization goal, constructing a third industrial objective function for the industrial microgrid, and constructing a third industrial constraint condition for the third industrial objective function according to the operation logic, operation time, task volume and storage condition of the industrial microgrid; Taking minimization of consumption data of the residential microgrid as an optimization goal, constructing a third resident objective function for the residential microgrid, and constructing a third resident constraint condition for the third resident objective function according to the power load of the residential microgrid; Taking minimization of the consumption data of the commercial microgrid as the optimization goal, a third business objective function for the commercial microgrid is constructed, and a third business constraint condition is constructed for the third business objective function based on the cooling energy and electric energy of the commercial microgrid.

6. The method according to claim 5, characterized in that The decoupling of the distribution network optimization objective function, the virtual power plant optimization objective function, and each microgrid optimization objective function into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant, and a third objective function corresponding to each microgrid includes: By using a first interconnected variable preset for the distribution network, a second interconnected variable preset for the virtual power plant, and a third interconnected variable preset for each microgrid, the distribution network optimization objective function, the virtual power plant optimization objective function, and each microgrid optimization objective function are decoupled to obtain a first objective function corresponding to the distribution network optimization objective function as shown below: The second objective function corresponding to the virtual power plant optimization objective function, The third industrial decoupling objective function corresponding to the third industrial objective function, A third resident decoupling objective function corresponding to the third resident objective function, a third business decoupling objective function corresponding to the third business objective function, Among them, K DN represents the first interconnected variable, K DR represents the second interconnected variable, nK WW represents n third interconnected variables, V t represents the first multiplication factor of time period t, W t represents the second multiplication factor of time period t; The first multiplier and the second multiplier satisfy the following formula: W t,k+1 =πW t,k Here, k represents the kth iteration.

7. A distribution network dispatching device containing multiple microgrids, characterized in that: include: A first optimization module, a second optimization module, a third optimization module and an iteration module; The first optimization module is configured to construct a distribution network optimization objective function with the goal of maximizing the return data of the distribution network, and to construct a first constraint condition for the distribution network optimization objective function based on the output of the wind turbine, the output of the gas turbine, and the capacity of the energy storage device; wherein the construction of the distribution network optimization objective function with the goal of maximizing the return data of the distribution network includes: With the goal of maximizing the return data of the distribution network, the distribution network optimization objective function is constructed as follows: Wherein, maxL1 represents the maximum return data of the distribution network, N represents the number of control scenarios when the distribution network distributes power, T represents the number of scenario periods when the distribution network distributes power, ω n represents the probability of scenario n occurring when the distribution network distributes power, represents the first partial return data of the distribution network's peak load regulation on the previous day, representing second local reporting data of the generator sets of the power distribution network, a third local reporting data indicating the frequency regulation reserve capacity of the distribution network, represents the first local consumption data of the distribution network's peak load regulation the day before, representing second local consumption data representing a generator set of said power distribution network, third local consumption data representing a frequency regulation reserve capacity of the distribution network; The second optimization module is configured to construct a virtual power plant optimization objective function with the goal of maximizing the return data of the virtual power plant, and to construct a second constraint condition for the virtual power plant optimization objective function according to the operating state of the gas turbine and the operating state of the energy storage device; The optimization module is configured to, for each microgrid, construct a respective microgrid optimization objective function for each microgrid with the goal of minimizing the consumption data of the microgrid, and construct a respective corresponding third constraint condition for each microgrid optimization objective function; The iteration module is configured to decouple the distribution network optimization objective function, the virtual power plant optimization objective function and the optimization objective function of each microgrid into a first objective function corresponding to the distribution network, a second objective function corresponding to the virtual power plant and a third objective function corresponding to each microgrid, construct a convergence condition using a preset convergence threshold, and optimize and iterate the first objective function, the second objective function and each third objective function until the convergence condition is met and the iteration is stopped, and power scheduling is performed based on the current first objective function, the second objective function and each third objective function.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the method according to any one of claims 1 to 6.

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