Micro-grid group and power distribution network collaborative optimization scheduling method and system

Abstract

The invention discloses a micro-grid group and power distribution network collaborative optimization scheduling method and system, and relates to the technical field of micro-grid groups and power distribution networks, and the method comprises the steps: obtaining the operation characteristics of operation data to construct energy storage models, and carrying out the aggregation of all energy storage models, and obtaining a flexible resource equivalent aggregation model; under a multi-dimensional constraint condition, taking the operation cost minimization of the power distribution network as a target function, and constructing a micro-grid group and power distribution network collaborative optimization scheduling model; and solving the micro-grid group and power distribution network collaborative optimization scheduling model by a linear solving method based on a Gaussian mixture model, so that the robustness and economy of the system can be ensured at the same time. The technical problems that an existing chance constraint planning method is low in calculation efficiency and results are more conservative when the number of samples is increased in various processing methods are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid groups and power distribution networks, and particularly relates to a micro-grid group and power distribution network collaborative optimization scheduling method and system. BACKGROUND

[0002] As the core carrier of distributed renewable energy consumption, the flexible adjustment capability of the power distribution network needs to be further enhanced to cope with the uncertainty of renewable energy output. As a small-scale power generation and distribution system integrating distributed power sources, energy storage devices and flexible resources, the micro-grid can effectively improve the flexibility of the power distribution network by operating in parallel with the power distribution network. The collaborative optimization scheduling of the micro-grid group and the power distribution network is the key path to realize the efficient application of distributed power sources and solve the problem of massive and diverse distributed power sources connected to the grid. This trend makes the research and development of the collaborative optimization scheduling technology of the micro-grid group and the power distribution network an important direction in the construction of new power systems.

[0003] At present, the existing optimization algorithms for the collaborative optimization scheduling of the micro-grid group and the power distribution network are mainly based on three types of scene random optimization, robust optimization and chance constraint programming. As an intuitive modeling method considering uncertainty risk, the chance constraint programming allows small probability violation of constraints, helping system operators balance robustness and optimality. However, the above chance constraint programming method has low computational efficiency and the result is more conservative when the number of samples increases. SUMMARY

[0004] The present application provides a micro-grid group and power distribution network collaborative optimization scheduling method and system, which solves the technical problem that the existing chance constraint programming method has low computational efficiency and the result is more conservative when the number of samples increases.

[0005] The present application provides a micro-grid group and power distribution network collaborative optimization scheduling method and system, which solves the technical problem that the existing chance constraint programming method has low computational efficiency and the result is more conservative when the number of samples increases.

[0006] Obtain the operation data corresponding to the electric vehicles and temperature-controlled loads in the micro-grid group, and construct an energy storage model according to the operation characteristics of the operation data, aggregate all the energy storage models to obtain an equivalent aggregated model of flexible resources;

[0007] Construct a multi-dimensional constraint condition according to the adjustment power parameters of the equivalent aggregated model of flexible resources;

[0008] Under the multi-dimensional constraint condition, construct a micro-grid group and power distribution network collaborative optimization scheduling model with the minimum operation cost of the power distribution network as the objective function;

[0009] Solve the micro-grid group and power distribution network collaborative optimization scheduling model to obtain a micro-grid group and power distribution network collaborative scheduling scheme.

[0010] Optionally, the step of acquiring the operating data corresponding to electric vehicles and temperature-controlled loads within the microgrid group, constructing an energy storage model based on the operating characteristics of the operating data, and aggregating all the energy storage models to obtain a flexibility resource equivalent aggregation model includes:

[0011] Acquire the operating data corresponding to electric vehicles and temperature-controlled loads within the microgrid group;

[0012] An electric vehicle-type energy storage model is constructed using the operational characteristics of the corresponding operating data of the electric vehicle.

[0013] A virtual energy storage model for the temperature-controlled load is constructed using the operating characteristics of the operating data corresponding to the temperature-controlled load.

[0014] By aggregating the electric vehicle-type energy storage model and the temperature-controlled load virtual energy storage model, a flexible resource equivalent aggregation model is obtained.

[0015] Optionally, the step of constructing multi-dimensional constraints based on the adjustment power parameters of the flexible resource equivalent aggregation model includes:

[0016] The flexible and adjustable constraints of the microgrid group are constructed based on the adjustable power parameters of the flexible resource equivalent aggregation model.

[0017] Based on the operating data corresponding to each node of the distribution network, the power flow constraints of the distribution network are constructed.

[0018] Using the node voltage data and branch power flow data of the distribution network, the system operation constraints of the distribution network are constructed.

[0019] Based on the active power output data and reactive power output data of the distributed generators in the power distribution network, construct the output constraints of the distributed generators in the power distribution network.

[0020] Based on the uncertainty of renewable energy, the output opportunity constraints of the renewable energy are constructed;

[0021] By combining the flexible and adjustable constraints, the power flow constraints, the system operation constraints, the distributed generator output constraints, and the renewable energy output opportunity constraints, multi-dimensional constraints are constructed.

[0022] Optionally, the objective function is specifically:

[0023]

[0024] In the formula, Electricity prices representing wholesale markets; This represents the amount of electricity purchased from the wholesale market; representing active power of the distributed generator; , and representing generation cost coefficient of the distributed generator; representing a set of nodes of the distribution network; representing unit cost of dispatching the microgrid group by the distribution network; representing active power exchanged between the microgrid and the distribution network.

[0025] Optionally, the linearization solving method based on the Gaussian mixture model is used to solve the microgrid group and distribution network collaborative optimization scheduling model to obtain a microgrid group and distribution network collaborative scheduling scheme, including:

[0026] The linearization solving method based on the Gaussian mixture model is used to linearize the output opportunity constraint condition of the renewable energy of the microgrid group and distribution network collaborative optimization scheduling model;

[0027] An optimization solver is used to solve the linearized microgrid group and distribution network collaborative optimization scheduling model to obtain a microgrid group and distribution network collaborative scheduling scheme.

[0028] Optionally, the linearization solving method based on the Gaussian mixture model is used to linearize the output opportunity constraint condition of the renewable energy of the microgrid group and distribution network collaborative optimization scheduling model, including:

[0029] The output opportunity constraint condition of the renewable energy of the microgrid group and distribution network collaborative optimization scheduling model is converted into a cumulative density function;

[0030] The cumulative density function is standardized to obtain a predicted error value between a wind turbine generator and a photovoltaic generator of the renewable energy after the standardization processing;

[0031] A Gaussian mixture model is used to fit the probability density function of the predicted error value between the wind turbine generator and the photovoltaic generator;

[0032] Based on parameters of the Gaussian mixture model, the fitted probability density function is converted into a linear combination of a plurality of Gaussian distribution cumulative density functions;

[0033] A translation method and a scaling method are used to perform piecewise linearization processing on the linear combination of the Gaussian distribution cumulative density functions.

[0034] The second aspect of the present application provides a microgrid group and distribution network collaborative optimization scheduling system, including:

[0035] The acquisition module is configured to acquire operation data corresponding to electric vehicles and temperature-controlled loads in the micro-grid group, and construct energy storage models according to operation characteristics of the operation data, aggregate all the energy storage models, and obtain an equivalent aggregated model of flexible resources.

[0036] The first construction module is configured to construct a multi-dimensional constraint condition according to an adjustment power parameter of the equivalent aggregated model of flexible resources.

[0037] The second construction module is configured to construct a collaborative optimization scheduling model of the micro-grid group and the power distribution network under the multi-dimensional constraint condition, with minimization of operation cost of the power distribution network as an objective function.

[0038] The solving module is configured to solve the collaborative optimization scheduling model of the micro-grid group and the power distribution network based on a linearization solving method of a Gaussian mixture model, and obtain a collaborative scheduling scheme of the micro-grid group and the power distribution network.

[0039] The third aspect of the present application provides an electronic device, comprising a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the micro-grid group and power distribution network collaborative optimization scheduling method according to any one of the above.

[0040] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to realize the micro-grid group and power distribution network collaborative optimization scheduling method according to any one of the above.

[0041] The fifth aspect of the present application provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the micro-grid group and power distribution network collaborative optimization scheduling method according to any one of the above.

[0042] From the above technical solutions, the present application has the following advantages:

[0043] The present application constructs energy storage models by using operation characteristics of operation data, aggregates all the energy storage models to obtain an equivalent aggregated model of flexible resources, constructs a multi-dimensional constraint condition according to an adjustment power parameter of the equivalent aggregated model of flexible resources, constructs a collaborative optimization scheduling model of the micro-grid group and the power distribution network under the multi-dimensional constraint condition, with minimization of operation cost of the power distribution network as an objective function, and solves the collaborative optimization scheduling model of the micro-grid group and the power distribution network based on a linearization solving method of a Gaussian mixture model to make the present application ensure robustness and economy of the system at the same time. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the steps of a method for coordinated optimization scheduling of microgrid groups and distribution networks according to Embodiment 1 of the present invention.

[0046] Figure 2 This is a flowchart illustrating the steps of a collaborative optimization scheduling method for microgrid groups and distribution networks provided in Embodiment 2 of the present invention.

[0047] Figure 3 This is a structural block diagram of a microgrid group and distribution network collaborative optimization scheduling system provided in Embodiment 3 of the present invention;

[0048] Figure 4 This is a structural block diagram of a computer device provided in Embodiment 4 of the present invention. Detailed Implementation

[0049] This invention provides a method and system for coordinated optimization scheduling of microgrid groups and distribution networks, which addresses the technical problem that existing opportunity-constrained programming methods are not only computationally inefficient among various processing methods, but also tend to produce more conservative results when the number of samples increases.

[0050] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0051] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for coordinated optimization scheduling of microgrid groups and distribution networks, as provided in Embodiment 1 of the present invention.

[0052] This invention provides a method for coordinated optimization scheduling of microgrid groups and distribution networks, comprising the following steps:

[0053] Step 101, obtaining the operation data corresponding to the electric vehicles and temperature-controlled loads in the micro-grid group respectively, and constructing an energy storage model according to the operation characteristics of the operation data, aggregating all the energy storage models to obtain an equivalent aggregation model of flexible resources.

[0054] Micro-grid group refers to a system formed by interconnecting multiple micro-grids through power lines and communication networks.

[0055] Electric vehicle (EV) operation data refers to a set of parameters used to represent the operating state and constraint conditions of an electric vehicle, including electric vehicle number (an identifier used to distinguish different electric vehicles), state of charge (the amount of electric energy currently stored in the battery of the electric vehicle), charging and discharging power (the amount of electric energy changed per unit time when the electric vehicle is charging or discharging), charging and discharging efficiency (the ratio of effective electric energy to input / output electric energy during the charging or discharging process of the electric vehicle), arrival / leave grid time (the time point at which the electric vehicle starts to be dispatchable after connecting to the grid and ends the dispatch after disconnecting from the grid), maximum charging and discharging power (the upper limit value of the charging or discharging power allowed by the electric vehicle), upper and lower limits of the state of charge (the highest and lowest values of the battery capacity allowed to be maintained during the operation of the electric vehicle), upper and lower limits of the battery capacity (the maximum and minimum ranges of the electric energy that can be stored determined by the physical characteristics of the battery), off-grid target electric quantity (the preset battery capacity to be reached when the electric vehicle is disconnected from the grid).

[0056] Temperature-controlled load operation data refers to parameters describing the working state and physical characteristics of temperature-controlled loads, including ambient temperature (the real-time temperature of the external environment in which the temperature-controlled load is located), temperature-controlled load output power (the electric energy consumed per unit time by the temperature-controlled load to maintain or adjust the temperature), temperature conversion coefficient (a coefficient representing the conversion relationship between the temperature-controlled load output power and temperature change), heat capacity (a physical quantity representing the heat storage capacity of the temperature-controlled load and the space in which it is located), thermal resistance (a physical quantity representing the heat transfer resistance of the temperature-controlled load and the space in which it is located), user-set ideal temperature (the temperature that the user expects the temperature-controlled load to maintain), baseline power (the constant power required to maintain the temperature of the space in which the temperature-controlled load is located at the ideal temperature), rated power (the standard power value of the temperature-controlled load when it is designed to operate).

[0057] Energy storage model refers to a mathematical model constructed based on the operation data and inherent operating characteristics of the two types of flexible resources, electric vehicles (EVs) and temperature-controlled loads, in the micro-grid group, for quantitatively representing their flexible adjustment capabilities, energy storage and release rules, and operating constraints.

[0058] Equivalent aggregation model of flexible resources refers to a model obtained by aggregating the flexible resource aggregation models of each node in the micro-grid group and combining the micro-grid topology power flow relationship to the coupling node connected to the distribution network.

[0059] In the embodiment of the present application, the operation data of the electric vehicles (EV) in the micro-grid group is obtained, including the number of the electric vehicles, the real-time power state, the charging and discharging power, the charging and discharging efficiency, the time of arriving and leaving the power grid, the maximum charging and discharging power, the upper and lower limits of the power state, the upper and lower limits of the battery capacity and the target power when off-grid, and the operation data of the temperature control load is also obtained, including the external environment temperature, the output power of the temperature control load, the temperature conversion coefficient, the heat capacity, the thermal resistance, the ideal temperature set by the user, the baseline power and the rated power for maintaining the ideal temperature; based on the operation characteristics of the electric vehicles, under the premise of ensuring the charging demand of the electric vehicles when off-grid, the single electric vehicle is regarded as a flexible energy storage unit, and according to the law of the power state changing with time, the charging and discharging power limit and the target power requirement when off-grid, an electric vehicle type energy storage model is constructed, which can calculate the flexible feasible region of the electric vehicle; for the temperature control load, the thermal storage characteristics of the building are used, and the temperature control load energy storage model based on virtual energy storage is constructed through the thermal dynamic law, the temperature change and the energy state. The electric vehicle type energy storage model and the temperature control load energy storage model based on virtual energy storage are aggregated to obtain an equivalent aggregation model of flexible resources.

[0060] Step 102, constructing a multi-dimensional constraint condition according to the adjustment power parameter of the equivalent aggregation model of flexible resources.

[0061] The adjustment power parameter refers to the key parameter extracted from the equivalent aggregation model of flexible resources, which represents the power interaction between the micro-grid group and the power distribution network, mainly including the aggregated active interaction power (the actual active power exchanged between the micro-grid group and the power distribution network), the reactive interaction power (the actual reactive power exchanged between the micro-grid group and the power distribution network), and the maximum and minimum adjustable limits of the two types of power.

[0062] The multi-dimensional constraint condition refers to a constraint set covering multiple operating dimensions, which is constructed based on the adjustment power parameter, combined with the safe operation requirements of the power distribution network and the adjustment capability of the micro-grid group. It usually includes active power balance constraints, reactive power constraints, node voltage safety constraints and energy state constraints.

[0063] In the embodiment of the present application, after obtaining the equivalent aggregation model of flexible resources, the adjustment power parameters of the coupling nodes between the micro-grid group and the power distribution network in the model are first extracted, including the aggregated active interaction power, the reactive interaction power and their adjustable ranges, and the multi-dimensional constraint condition is constructed by combining the safe operation requirements of the power distribution network and the actual adjustment capability of the flexible resources in the micro-grid group.

[0064] Step 103, constructing a micro-grid group and power distribution network collaborative optimization scheduling model with the minimum operation cost of the power distribution network as the objective function under the multi-dimensional constraint condition.

[0065] The operation cost of the power distribution network refers to the total sum of various economic expenditures generated in the operation process of the power distribution network, including the cost of purchasing electricity from the wholesale market, the power generation cost of the distributed generator, and the fee for scheduling the micro-grid group.

[0066] The objective function refers to a mathematical expression used to quantify the optimization objective of minimizing the operation cost of the power distribution network in the collaborative optimization scheduling model.

[0067] The collaborative optimization scheduling model of the micro-grid group and the power distribution network refers to a mathematical model used to schedule the operation states of the micro-grid group and the power distribution network as a whole, with the minimization of the operation cost of the power distribution network as the objective function, and multi-dimensional constraint conditions, DG output constraints, and renewable energy opportunity constraints as boundary conditions.

[0068] In the embodiment of the present application, after determining the multi-dimensional constraint conditions, the composition dimensions of the operation cost of the power distribution network are first determined, mainly including the cost of purchasing electricity from the wholesale market, the power generation cost of the distributed generator (DG), and the fee for scheduling the micro-grid group, and a target function with the minimization of the operation cost of the power distribution network as the core is constructed based on this; subsequently, the multi-dimensional constraint conditions (including active power balance constraints, reactive power constraints, node voltage safety constraints, and energy state constraints) constructed previously are taken as the constraint boundaries of the target function, and the upper and lower limits of the output of the distributed generator (to ensure that the DG power output is within the safe operation range) and the opportunity constraints of the renewable energy output (to control the constraint violation probability within the allowable risk range by processing the photovoltaic and wind power prediction errors through the Gaussian mixture model) are also integrated, so that the optimization process of the target function not only meets the safe operation requirements of the power distribution network, but also is compatible with the regulation characteristics of the micro-grid group and the uncertainty of renewable energy; finally, the target function and the full constraint conditions are integrated to form a complete collaborative optimization scheduling model of the micro-grid group and the power distribution network.

[0069] Step 104: Based on the linearization solving method of the Gaussian mixture model, the collaborative optimization scheduling model of the micro-grid group and the power distribution network is solved to obtain a collaborative scheduling scheme of the micro-grid group and the power distribution network.

[0070] The Gaussian mixture model (GMM, Gaussian Mixture Model) refers to a probability model composed of multiple Gaussian sub-distributions combined by weighting, wherein each sub-distribution has an independent weight coefficient, mean value, and variance, and in theory, any complex probability density function can be fitted by adjusting the parameters, which is used in the present application to accurately represent the non-specific distribution characteristics of the prediction error of renewable energy sources such as photovoltaic and wind power.

[0071] Linearization solution method refers to a method of transforming the non-linear constraint conditions (such as the cumulative density function of the Gaussian distribution involved in the chance constraint) in the model into linear constraints through mathematical transformation (such as piecewise linear approximation, introduction of auxiliary variables), so that the originally difficult-to-solve non-linear model becomes a linear model that can be efficiently solved by a commercial solver.

[0072] Micro-grid group and distribution network collaborative scheduling scheme refers to a scheduling scheme obtained by solving a collaborative optimization scheduling model, covering key parameters such as the distribution network's purchase of electricity from the wholesale market, the distributed generator's output plan, and the active / reactive power interaction plan between the micro-grid group and the distribution network.

[0073] In the embodiment of the present application, after the micro-grid group and distribution network collaborative optimization scheduling model is constructed, firstly, the Gaussian mixture model (GMM) is used to fit the probability density function of the prediction error for the renewable energy output chance constraint contained in the model, and by adjusting the sub-distribution weight coefficient, mean and variance parameters of the GMM, the prediction error with complex and non-specific distribution in practice is accurately characterized; then, the chance constraint is transformed into an expression form based on the cumulative density function, and the cumulative density function is decomposed into a weighted sum of multiple Gaussian sub-distribution cumulative density functions by means of the parameters of the GMM, since the cumulative density function of a single Gaussian distribution has an explicit expression but is non-convex and nonlinear, a piecewise linear model is further used to approximate the cumulative density function of the standard Gaussian distribution, which is transformed into a linear expression by determining the piecewise linear segment parameters, and then through translation and scaling processing, the linear approximation form of all GMM sub-distribution cumulative density functions is obtained; then, auxiliary variables are introduced to combine the linearized cumulative density function expression with the chance constraint, and finally the non-linear chance constraint in the original model which is difficult to solve directly is transformed into a linear constraint, so that the entire collaborative optimization scheduling model becomes a linear model that can be efficiently solved; finally, the existing commercial solver is used to solve the linearized model, and the distribution network's purchase of electricity from the wholesale market, the distributed generator's output plan, and the active and reactive power interaction plan between the micro-grid group and the distribution network are output as key scheduling parameters, and the integration of these parameters obtains the micro-grid group and distribution network collaborative scheduling scheme that meets the minimum objective of the distribution network operation cost and conforms to the multi-dimensional constraint conditions.

[0074] In the embodiment of the present application, the present application constructs an energy storage model by using the operation characteristics of operation data, aggregates all energy storage models to obtain an equivalent aggregated model of flexible resources, constructs a multi-dimensional constraint condition according to the adjustment power parameter of the equivalent aggregated model of flexible resources, constructs a micro-grid group and distribution network collaborative optimization scheduling model under the multi-dimensional constraint condition, taking the minimization of the operation cost of the distribution network as the objective function, and solves the micro-grid group and distribution network collaborative optimization scheduling model based on the linearization solving method of the Gaussian mixture model to ensure the robustness and economy of the system.

[0075] Please refer to Figure 2 , Figure 2 The step flow chart of a micro-grid group and distribution network collaborative optimization scheduling method provided for the second embodiment of the present application is shown in the figure.

[0076] The micro-grid group and distribution network collaborative optimization scheduling method provided by the present application comprises the following steps:

[0077] Step 201: Obtain the operation data corresponding to the electric vehicles and temperature-controlled loads in the micro-grid group respectively, and construct an energy storage model according to the operation characteristics of the operation data, aggregate all energy storage models to obtain an equivalent aggregated model of flexible resources.

[0078] Further, step 201 comprises the following sub-steps:

[0079] S11: Obtain the operation data corresponding to the electric vehicles and temperature-controlled loads in the micro-grid group respectively;

[0080] In the embodiment of the present application, the real-time monitoring equipment and the data acquisition system deployed in the micro-grid group are used to collect the operation data of the electric vehicles and the temperature-controlled loads respectively. For the electric vehicles, the unique number of each electric vehicle (used to distinguish different vehicles), the current power state (the remaining battery power), the real-time charging and discharging power (the power value when charging or discharging), the charging and discharging efficiency (the power conversion efficiency), the access time to the power grid and the off-grid time (the boundary of the scheduling period), the maximum charging and discharging power allowed by the equipment (the upper limit of safe operation), the upper and lower limits of the battery power state (to avoid overcharging and over-discharging), and the target power required when off-grid (to ensure the user's travel demand) are obtained in real time by relying on the communication module of the vehicle terminal and the power grid interaction interface. For the temperature-controlled loads, the external environment temperature (which affects the power demand of the load), the current output power of the temperature-controlled load (the power consumption for maintaining or adjusting the temperature), the inherent temperature conversion coefficient of the equipment (the conversion relationship between power and temperature change), the heat capacity and thermal resistance (the building heat storage and heat transfer characteristic parameters), the ideal temperature set by the user (the load adjustment target), the baseline power required to maintain the ideal temperature (the basic power consumption), and the rated power of the temperature-controlled load (the upper limit of the design power of the equipment) are collected by the temperature sensor, the power metering device and the load control terminal in the building. At the same time, the collected operation data of the two types is subjected to outlier rejection and data cleaning.

[0081] S12, constructing an electric vehicle type energy storage model using the operation characteristics of the operation data corresponding to the electric vehicle;

[0082] The electric vehicle type energy storage model refers to a mathematical model constructed based on the operation data and operation characteristics of the electric vehicle.

[0083] In the embodiment of the present application, a single EV can be regarded as a flexible energy storage unit when it is connected to the power grid. Under the premise that the charging demand of the EV can be met when it leaves, the power of the charging process of the EV can be scheduled. The flexibility feasible region of a single EV when it is connected to the power grid can be calculated according to the energy storage model shown in the following formula, i.e. the electric vehicle type energy storage model.

[0084] It should be noted that the electric vehicle type energy storage model is specifically shown in the following formula:

[0085] (1a)

[0086] (1b)

[0087] (1c)

[0088] (1d)

[0089] (1e)

[0090] where i is the number of EVs; is the state of charge of EVs; is the charging / discharging power of EVs; is the charging / discharging efficiency of EVs; is the arrival / departure time of EVs; is the maximum charging / discharging power of EVs; is the upper / lower limit of the state of charge of EVs; is the upper / lower limit of the battery capacity of EVs; is the target state of charge of EVs when off-grid, and t is the number of scheduling periods, is the length of the scheduling period, is the energy level of the i-th EV when the vehicle starts charging at the arrival time of the grid, is the energy level of the i-th EV when the vehicle starts charging.

[0091] S13, using the operating characteristics of the temperature-controlled load corresponding to the operating data, constructing a temperature-controlled load virtual energy storage model;

[0092] The temperature-controlled load virtual energy storage model refers to a mathematical model constructed by mapping temperature changes as "virtual energy state" based on the thermal dynamic characteristics of the temperature-controlled load.

[0093] In the embodiments of the present application, because the building itself has heat storage capacity, the thermal dynamics of the temperature-controlled load can be modeled as follows:

[0094] (2a)

[0095] (2b)

[0096] where, represents the ambient temperature; represents the indoor temperature of the building; is the output power of the temperature-controlled load; is the temperature conversion coefficient of the temperature-controlled load; is the heat capacity of the temperature-controlled load; is the thermal resistance of the temperature-controlled load; and are coefficients related to the heat capacity and the thermal resistance.

[0097] It should be noted that the thermal dynamic model can be represented as a virtual energy storage-based characterization model as follows, and the temperature-controlled load virtual energy storage model is specifically shown by the following formula:

[0098] (2c)

[0099] (2d)

[0100] (2e)

[0101] (2f)

[0102] (2g)

[0103] (2h)

[0104] (2i)

[0105] (2j)

[0106] In the formula, represents the energy state of virtual energy storage; represents the output power of virtual energy storage; represents the ideal temperature set by the user; is the baseline power for maintaining the temperature at ; represents the upper and lower limits of the virtual energy storage output power; represents the upper and lower limits of the virtual energy storage energy state; represents the temperature variation range of operation; is the rated power of the temperature-controlled load.

[0107] S14, aggregating the electric vehicle type energy storage model and the temperature-controlled load virtual energy storage model to obtain an equivalent aggregation model of flexible resources.

[0108] In the embodiments of the present application, the electric vehicle type energy storage flexibility model (1) and the temperature-controlled load virtual energy storage flexibility model (2) established in steps S12-S13 can be uniformly modeled as the following standard form based on a polyhedron:

[0109] (3a)

[0110] (3b)

[0111] (3c)

[0112] (3d)

[0113] (3e)

[0114] In the formula, represents the flexible feasible region; and represents the charging and discharging power; and represents the charging and discharging efficiency; represents the energy dissipation coefficient, is a first coefficient matrix, is the power of the i-th flexible resource, is a constraint coefficient matrix, is the maximum charging power of the i-th flexible resource, is the maximum discharging power of the i-th flexible resource, is the minimum charging power of the i-th flexible resource at time t, is the minimum discharging power of the i-th flexible resource at time t, is the inverse matrix of matrix , used for inverse operation in energy state and related calculations to reflect the reverse relationship of the energy change of the flexible resource, is the initial energy state vector of the i-th flexible resource, is a diagonal matrix of the charging efficiency of the i-th flexible resource, is a diagonal matrix of the discharging efficiency of the i-th flexible resource.

[0115] It should be noted that based on the standard form of the flexible model, the aggregated adjustable capacity of the flexible resource under each node in the microgrid can be equivalently modeled (i.e., the equivalent aggregated model of the flexible resource) as follows:

[0116] (3f)

[0117] (3g)

[0118] In the formula, represents the flexible feasible region under a node of the microgrid group; and represents the aggregated charging and discharging power; represents the number of flexible resources, is a vector composed of the aggregated charging and discharging power.

[0119] It is worth mentioning that after completing the resource aggregation of a single node in the microgrid group, based on the topological flow relationship of the microgrid, the adjustable capacity of the single node of the microgrid represented by formula (3f) and formula (3g) can be converged to the coupling node of the microgrid group connected with the power distribution network, and the flexible adjustable capacity of the coupling node of the microgrid group connected with the power distribution network can be modeled as follows:

[0120] (3h)

[0121] In the formula, and This represents the active and reactive power interaction between the microgrid and the distribution network; , and It is a constant term related to the parameters of the microgrid system.

[0122] Step 202: Construct multi-dimensional constraints based on the adjustment power parameters of the flexible resource equivalent aggregation model.

[0123] Further, step 202 includes the following sub-steps:

[0124] S21. Construct flexible and adjustable constraints for microgrid groups based on the adjustable power parameters of the flexible resource equivalent aggregation model.

[0125] The multi-dimensional constraints include system operation constraints of the distribution network, output constraints of distributed generators, flexible and adjustable constraints of microgrids, and opportunity constraints of renewable energy output.

[0126] Flexible and adjustable constraints refer to a set of constraints constructed based on adjustable power parameters to limit the power interaction and adjustment process between the microgrid and the distribution network.

[0127] In this embodiment of the invention, the flexible and adjustable constraints of the microgrid group are constrained by equation (3h) modeled in step S14:

[0128] (4a)

[0129] In the formula, and This represents the active and reactive power interaction between the microgrid and the distribution network; , and It is a constant term related to the parameters of the microgrid system.

[0130] S22. Based on the operating data corresponding to each node of the distribution network, construct the power flow constraints of the distribution network.

[0131] The operational data corresponding to each node in the distribution network refers to the set of key parameters generated by each power node in the distribution network during operation. The core parameters include node voltage amplitude, node voltage phase angle, node injected active power, node injected reactive power, as well as the branch resistance and branch reactance of the connecting nodes.

[0132] Power flow constraints refer to the set of constraints constructed based on the operating data of each node in the distribution network and the power flow theory of the power system.

[0133] In this embodiment of the invention, the power flow constraints of the distribution network are as follows:

[0134] (4b)

[0135] (4c)

[0136] (4d)

[0137] (4e)

[0138] (4f)

[0139] where, represents the voltage amplitude of the node; represents the phase angle of the voltage of the node; represents the node adjacent to the node ; and represent the active and reactive load of the node, respectively; and represent the injection power of the node; and represent the active and reactive power flow of the branch; and represent the output of the distributed photovoltaic and wind turbine, respectively; and represent the per-unit value of resistance and reactance, respectively; formula (4d) and (4e) represent the node power balance constraint, is the active / reactive power of the distributed generator, is the active / reactive power of the upper main grid, is the active / reactive power of the transmission grid, wherein i and j are both node numbers, i is the first index of the node, and j is the second index of the node, is the second coefficient of the branch ij, which is related to the reactance and other parameters of the branch, and is used for active power flow calculation of the branch, is the per-unit value of the reactance of the branch ij, which is a parameter used to quantify the size of the reactance of the branch in power system analysis, is the voltage amplitude of the node j at time t, which is a physical quantity describing the size of the voltage of the node, is the first coefficient of the branch ij, which is related to the resistance, reactance and other parameters of the branch, is the phase angle of the voltage of the node j at time t, which is a physical quantity describing the phase of the voltage of the node.

[0140] S23, using the node voltage data and branch power flow data of the system of the distribution network, constructing the system operation constraint condition of the distribution network;

[0141] The node voltage data refers to the set of voltage related parameters of all power nodes in the distribution network during operation.

[0142] Branch flow data refers to a set of power transmission parameters of power branches connecting nodes in the distribution network during operation.

[0143] System operation constraints refer to a set of constraints constructed based on node voltage data and branch flow data, combined with safe operation requirements of the distribution network.

[0144] In the embodiments of the present application, the node voltage and branch flow of the system need to satisfy the following safety constraints, i.e., system operation constraints:

[0145] (4g)

[0146] (4h)

[0147] (4i)

[0148] In the formula, and represent the upper and lower limits of system voltage and branch flow.

[0149] S24, according to the active power output data and the reactive power output data of the distributed generators of the distribution network, constructing the distributed generator output constraints of the distribution network;

[0150] Active power output data refers to a set of parameters of active power output characteristics of the distributed generator, including real-time active power, maximum / minimum active power limit, active power adjustment rate.

[0151] Reactive power output data refers to a set of parameters of reactive power output characteristics of the distributed generator, including real-time reactive power, maximum / minimum reactive power limit, reactive power adjustment rate.

[0152] Distributed generator output constraints refer to a set of constraints constructed based on active and reactive power output data and equipment characteristics of the distributed generator, used to limit the active and reactive output range and adjustment rhythm, and ensure safe operation of the equipment, mainly including active power upper and lower limit constraints, active power change rate constraints, reactive power upper and lower limit constraints, reactive power change rate constraints and active-reactive coupling constraints.

[0153] In the embodiments of the present application, the active and reactive power output constraints of the DG are as follows:

[0154] (4j)

[0155] (4k)

[0156] In the formula, and represent the upper and lower limits of DG power.

[0157] S25, constructing an output opportunity constraint condition of the renewable energy according to uncertainty of the renewable energy;

[0158] The uncertainty of the renewable energy refers to the characteristics that the actual output of the renewable energy such as photovoltaic and wind power is deviated from the predicted output due to the random changes of natural conditions such as light intensity, wind speed and weather, and the output is difficult to accurately predict and has a large fluctuation range, which is a problem that needs to be focused on by power distribution network dispatching.

[0159] The output opportunity constraint condition of the renewable energy refers to a probabilistic constraint that considers the uncertainty of the output of the renewable energy and allows a small probability (allowed risk probability ε) to violate the output constraint.

[0160] In the embodiment of the present application, considering the uncertainty of the renewable energy, the output of the photovoltaic power generation system and the wind turbine is constrained by the following opportunity constraint:

[0161] (4m)

[0162] (4n)

[0163] In the formula, and represent the predicted values of the output of the photovoltaic and the wind turbine; and represent the prediction errors; is the allowed risk probability.

[0164] S26, combining the flexible adjustable constraint condition, the power flow constraint condition, the system operation constraint condition, the distributed generator output constraint condition and the output opportunity constraint condition of the renewable energy, a multi-dimensional constraint condition is constructed.

[0165] In the embodiment of the present application, after obtaining the flexible adjustable constraint condition, the power flow constraint condition, the system operation constraint condition, the distributed generator output constraint condition and the output opportunity constraint condition of the renewable energy, the role boundary and the correlation of each constraint condition in the collaborative scheduling are first determined, and then the multi-dimensional constraint is organically integrated through parameter mapping and logic integration, that is, the multi-dimensional constraint condition is obtained.

[0166] Step 203, under the multi-dimensional constraint condition, a microgrid group and power distribution network collaborative optimization scheduling model is constructed with the minimum operation cost of the power distribution network as the objective function.

[0167] In the embodiment of the present application, after the multi-dimensional constraint condition is determined, a target function is constructed based thereon, with minimization of power distribution network operation cost as the core, so that the optimization process of the target function not only meets the safe operation requirements of the power distribution network, but also is compatible with the regulation characteristics of the micro-grid group and the uncertainty of renewable energy; finally, by integrating the target function and the multi-dimensional constraint condition, a complete micro-grid group and power distribution network collaborative optimization scheduling model is formed.

[0168] It should be noted that the target function is specifically:

[0169] (4o)

[0170] In the formula, represents the electricity price of the wholesale market; represents the electricity quantity purchased from the wholesale market; represents the active power of the distributed generator; , and represents the power generation cost coefficient of the distributed generator; represents the node set of the distribution network; represents the unit cost of the power distribution network scheduling the micro-grid group; and represent the active and reactive power exchanged between the micro-grid and the power distribution network.

[0171] Step 204, based on the linearization solving method of the Gaussian mixture model, linearizes the output opportunity constraint condition of the renewable energy of the micro-grid group and the power distribution network collaborative optimization scheduling model.

[0172] Further, step 204 includes the following sub-steps:

[0173] S31, convert the output opportunity constraint condition of the renewable energy of the micro-grid group and the power distribution network collaborative optimization scheduling model into a cumulative density function;

[0174] Cumulative density function (Cumulative Distribution Function, CDF) refers to a function describing the probability of a random variable (such as renewable energy prediction error) taking a value less than or equal to a certain specific value.

[0175] In the embodiment of the present application, the opportunity constraint shown in formula (4m) and formula (4n) in the above-mentioned micro-grid group and power distribution network collaborative optimization scheduling model lacks an explicit analytical expression, which makes the model difficult to solve. The opportunity constraint linearization solving method based on Gaussian mixture model (Gaussian mixture model, GMM) proposed in this step.

[0176] It should be noted that first convert formula (4m) and formula (4n) into the following expression based on the cumulative density function:

[0177] (5a)

[0178] (5b)

[0179] In the formula, And The predicted value of the output of photovoltaic and wind turbine.

[0180] S32, the cumulative density function is standardized to obtain the predicted error value between the wind turbine and the photovoltaic generator of the renewable energy after standardization processing;

[0181] Standardization processing refers to the process of mapping random variables (such as prediction errors) to standard normal distribution (mean value is 0, standard deviation is 1) through mathematical conversion.

[0182] The predicted error value refers to the difference between the actual output and the predicted output of the renewable energy (actual output-predicted output), which is a key parameter for characterizing the uncertainty of renewable energy output.

[0183] In the embodiment of the application, the difficulty of solving the above opportunity constraint lies in how to convert the cumulative density function of the uncertain variable into a linear solvable expression. In order to facilitate the following theory and formula derivation, formula (5a) and formula (5b) are expressed as the following unified standard form:

[0184] (5c)

[0185] In the formula, The predicted error of the wind turbine and the photovoltaic generator; The difference between the planned scheduling power and the day-ahead predicted maximum power of the wind turbine and the photovoltaic generator.

[0186] S33, the probability density function of the predicted error value between the wind turbine and the photovoltaic generator is fitted by using the Gaussian mixture model;

[0187] The probability density function refers to the function describing the probability of a random variable (such as prediction error) in a unit interval near a certain value point.

[0188] Fitting processing refers to the process of adjusting model parameters to make the probability density function generated by the model as close as possible to the distribution characteristics of sample data (such as predicted error value).

[0189] In this embodiment of the invention, since GMM can theoretically achieve accurate fitting of any distribution function by adjusting parameters, the probability density function of the prediction error of wind turbines and photovoltaic generators can be fitted using the GMM shown below:

[0190] (5d)

[0191] (5e)

[0192] In the formula, Indicates the first GMM Weighting coefficients of individual sub-distributions; The mean and variance are respectively and The Gaussian distribution.

[0193] S34. Based on the parameters of the Gaussian mixture model, the fitted probability density function is transformed into a linear combination of multiple Gaussian cumulative density functions;

[0194] The cumulative density function of a Gaussian distribution refers to the cumulative density function of a single Gaussian distribution, which describes the probability that a random variable under this distribution takes a value less than or equal to a certain specific value.

[0195] Linear combination refers to a mathematical form that uses weighted summation of the cumulative density functions of multiple Gaussian distributions through weighting coefficients.

[0196] In this embodiment of the invention, based on the parameters of the GMM, equation (5c) can be expressed as a linear combination of multiple Gaussian cumulative density functions:

[0197] (5f)

[0198] In the formula, The mean and variance are respectively and The cumulative density function of the Gaussian distribution.

[0199] It is worth mentioning that, although the cumulative density function of the Gaussian function While it has a clear expression, its non-convex and nonlinear properties still make the model difficult to solve. To transform equation (5f) into a linear form, the piecewise linearization model shown in equation (5g) can be used for the standard Gaussian distribution. Approximation:

[0200] (5g)

[0201] In the formula, and For the first a parameter of a line segment, the line segment being determined by two different points on the line segment; denotes the number of segments.

[0202] S35, the linear combination of the Gaussian distribution cumulative density functions is segmented linearized by using a translation method and a scaling method.

[0203] The translation method refers to an operation of eliminating the difference in the central position of the Gaussian sub-distribution cumulative density function (CDF) by subtracting the mean of the random variable from the random variable in mathematical processing.

[0204] The scaling method refers to an operation of eliminating the difference in the dispersion degree of the Gaussian sub-distribution CDF by dividing the translated variable by the standard deviation of the translated variable.

[0205] The segmented linearization processing refers to a processing method of dividing a continuous nonlinear function (such as a Gaussian distribution CDF) into a plurality of intervals in its definition domain, and approximating each interval by a linear segment.

[0206] In the embodiment of the application, after obtaining the segmented linear approximation expression of the cumulative density function of the standard Gaussian distribution, the translation and scaling can obtain the segmented linearization expression of

[0207] (5h)

[0208] It should be noted that finally, by introducing an auxiliary variable and combining equation (5h), equation (5f) can be converted into the following linear form:

[0209] (5i)

[0210] (5j)

[0211] (5k)

[0212] According to the above transformation method based on the Gaussian mixture model, the chance constraint represented by equation (4m) and equation (4n) which lacks an explicit expression can be converted into a linear form which is convenient to solve.

[0213] Step 205, using an optimization solver to solve the micro-grid group and power distribution network collaborative optimization scheduling model after linearization processing, to obtain a micro-grid group and power distribution network collaborative scheduling scheme.

[0214] The optimization solver refers to a professional software or algorithm tool for solving mathematical optimization models (such as linear programming, mixed integer programming models).

[0215] ​In the embodiment of the present application, after completing the linearization processing of the micro-grid group and power grid collaborative optimization scheduling model (including piecewise linearization of renewable energy output opportunity constraints), the objective function of the model (such as minimization of total cost of power grid operation, covering purchase of electricity, distributed power generation cost, flexible resource adjustment cost, etc.) and the linearized multi-dimensional constraint condition (including flexible adjustable constraint, power flow constraint, system operation constraint, etc.) are integrated into a standard mixed integer linear programming model; then an optimization solver suitable for large-scale linear programming problems is selected, and parameters such as solving accuracy and upper limit of iteration number are set, and the model is input into the solver in a standard data format; the solver solves the model by branch and bound, cutting plane and other algorithms, and finds the optimal decision variable value that makes the objective function optimal under the premise of meeting all linear constraints, including micro-grid and power grid interaction power plan, distributed generator (including photovoltaic, wind power) output plan, electric vehicle and temperature-controlled load charging / discharging / power regulation plan, node voltage and branch power flow optimization value, etc.; finally, the optimal decision variable combination output by the solver forms a micro-grid group and power grid collaborative scheduling scheme including power scheduling plan, flexible resource regulation strategy, and distributed power output arrangement, which not only meets all safety constraints and uncertainty risk control requirements, but also realizes the optimal operation economy, and can provide direct guidance for actual scheduling operation.

[0216] In the embodiment of the present application, the present application constructs an energy storage model by using the operating characteristics of the operating data, aggregates all energy storage models to obtain an equivalent aggregated model of flexible resources, constructs a multi-dimensional constraint condition according to the adjustment power parameters of the equivalent aggregated model of flexible resources, and constructs a micro-grid group and power grid collaborative optimization scheduling model with the minimization of the operation cost of the power grid as an objective function under the multi-dimensional constraint condition.

[0217] Please refer to Figure 3 , Figure 3 A structural block diagram of a micro-grid group and power grid collaborative optimization scheduling system provided for the third embodiment of the present application.

[0218] The micro-grid group and power grid collaborative optimization scheduling system provided by the present application comprises:

[0219] The acquisition module 301 is configured to acquire operating data corresponding to electric vehicles and temperature-controlled loads in the micro-grid group, construct an energy storage model according to operating characteristics of the operating data, aggregate all energy storage models to obtain an equivalent aggregated model of flexible resources, and obtain the equivalent aggregated model of flexible resources.

[0220] The first construction module 302 is configured to construct a multi-dimensional constraint condition according to an adjustment power parameter of a flexible resource equivalent aggregation model.

[0221] The second construction module 303 is configured to construct a micro-grid group and distribution network collaborative optimization scheduling model with a minimum operation cost of the distribution network as an objective function under the multi-dimensional constraint condition.

[0222] The solving module 304 is configured to solve the micro-grid group and distribution network collaborative optimization scheduling model based on a linearization solving method of a Gaussian mixture model to obtain a micro-grid group and distribution network collaborative scheduling scheme.

[0223] Further, the acquisition module 301 comprises:

[0224] The acquisition sub-module is configured to acquire operation data corresponding to the electric vehicles and the temperature-controlled loads in the micro-grid group.

[0225] The sub-module is configured to construct an electric vehicle type energy storage model by using operation characteristics of the operation data corresponding to the electric vehicles.

[0226] The temperature-controlled load sub-module is configured to construct a temperature-controlled load virtual energy storage model by using operation characteristics of the operation data corresponding to the temperature-controlled loads.

[0227] The equivalent aggregation sub-module is configured to aggregate the electric vehicle type energy storage model and the temperature-controlled load virtual energy storage model to obtain the flexible resource equivalent aggregation model.

[0228] Further, the first construction module 302 comprises:

[0229] The flexible adjustable sub-module is configured to construct a flexible adjustable constraint condition of the micro-grid group according to the adjustment power parameter of the flexible resource equivalent aggregation model.

[0230] The power flow sub-module is configured to construct a power flow constraint condition of the distribution network based on operation data corresponding to each node of the distribution network.

[0231] The system operation sub-module is configured to construct a system operation constraint condition of the distribution network by using node voltage data and branch power flow data of a system of the distribution network.

[0232] The distributed generator sub-module is configured to construct a distributed generator output constraint condition of the distribution network according to active power output data and reactive power output data of distributed generators of the distribution network.

[0233] The output opportunity sub-module is configured to construct an output opportunity constraint condition of the renewable energy according to uncertainty of the renewable energy.

[0234] A multi-dimensional sub-module is configured to combine flexible adjustable constraint conditions, power flow constraint conditions, system operation constraint conditions, distributed generator output constraint conditions and renewable energy output opportunity constraint conditions to construct multi-dimensional constraint conditions.

[0235] Further, the objective function is specifically as follows:

[0236]

[0237] In the formula, represents the electricity price of the wholesale market; represents the electricity quantity purchased from the wholesale market; represents the active power of the distributed generator; and represents the power generation cost coefficient of the distributed generator; represents the node set of the distribution network; represents the unit cost of the distribution network for scheduling the micro-grid group; represents the active power exchanged between the micro-grid and the distribution network.

[0238] Further, the solving module 304 comprises:

[0239] A linearization sub-module is configured to linearize the renewable energy output opportunity constraint condition of the micro-grid group and the distribution network collaborative optimization scheduling model based on a linearization solving method of a Gaussian mixture model.

[0240] A solving sub-module is configured to solve the linearized micro-grid group and the distribution network collaborative optimization scheduling model by using an optimization solver to obtain a micro-grid group and distribution network collaborative scheduling scheme.

[0241] Further, the linearization sub-module comprises:

[0242] A cumulative density function sub-module is configured to convert the renewable energy output opportunity constraint condition of the micro-grid group and the distribution network collaborative optimization scheduling model into a cumulative density function.

[0243] A standardization sub-module is configured to standardize the cumulative density function to obtain a predicted error value between the wind turbine and the photovoltaic generator of the renewable energy after standardization.

[0244] A fitting processing sub-module is configured to fit the probability density function of the predicted error value between the wind turbine and the photovoltaic generator by using a Gaussian mixture model.

[0245] A conversion sub-module is configured to convert the fitted probability density function into a linear combination of a plurality of Gaussian distribution cumulative density functions based on parameters of the Gaussian mixture model. ​

[0246] The linearization processing submodule is configured to perform piecewise linearization processing on the linear combination of the Gaussian distribution cumulative density functions by using a translation method and a scaling method.

[0247] Referring to Figure 4 , Figure 4 A structural block diagram of a computer device provided for the fourth embodiment of the present application.

[0248] The electronic device of the embodiment of the present application comprises a memory 401 and a processor 402, the memory 401 stores a computer program; the computer program is executed by the processor 402, so that the processor 402 executes the micro-grid group and power distribution network collaborative optimization scheduling method of any one of the above embodiments.

[0249] The memory 401 can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk or a ROM. The memory 401 has a storage space 403 for program codes 413 for executing any of the method steps described above. For example, the storage space 403 for program codes can include individual program codes 413 for implementing various steps in the above method, respectively. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program codes can be compressed in a suitable form, for example. These codes, when executed by a computing processing device, cause the computing processing device to perform the individual steps in the above described method. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program codes can be compressed in a suitable form, for example. These codes, when executed by a computing processing device, cause the computing processing device to perform the individual steps in the above described micro-grid group and power distribution network collaborative optimization scheduling method.

[0250] The fifth embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the micro-grid group and power distribution network collaborative optimization scheduling method of any one of the embodiments of the present application.

[0251] The sixth embodiment of the present application also provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the micro-grid group and power distribution network collaborative optimization scheduling method of any one of the above embodiments.

[0252] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0253] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic, and the division of the units is merely a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0254] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0255] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0256] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that makes a contribution to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0257] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for coordinated optimization and dispatching of microgrid clusters and distribution networks, characterized in that, The method comprises the following steps: acquiring operation data corresponding to electric vehicles and temperature-controlled loads in the micro-grid group, and constructing energy storage models according to operation characteristics of the operation data, aggregating all the energy storage models to obtain an equivalent aggregated model of flexible resources; constructing multi-dimensional constraint conditions according to adjustment power parameters of the equivalent aggregated model of flexible resources; under the multi-dimensional constraint conditions, constructing a collaborative optimization scheduling model of the micro-grid group and the power distribution network with minimization of operation cost of the power distribution network as an objective function; solving the collaborative optimization scheduling model of the micro-grid group and the power distribution network based on a linearization solving method of a Gaussian mixture model to obtain a collaborative scheduling scheme of the micro-grid group and the power distribution network. 2.The microgrid cluster and power grid collaborative optimization scheduling method of claim 1, wherein, The method of acquiring operation data corresponding to electric vehicles and temperature-controlled loads in the micro-grid group, and constructing energy storage models according to operation characteristics of the operation data, aggregating all the energy storage models to obtain an equivalent aggregated model of flexible resources comprises: acquiring operation data corresponding to electric vehicles and temperature-controlled loads in the micro-grid group; constructing an electric vehicle type energy storage model according to operation characteristics of the operation data corresponding to the electric vehicles; constructing a temperature-controlled load virtual energy storage model according to operation characteristics of the operation data corresponding to the temperature-controlled loads; aggregating the electric vehicle type energy storage model and the temperature-controlled load virtual energy storage model to obtain the equivalent aggregated model of flexible resources. 3.The microgrid cluster and power grid collaborative optimization scheduling method of claim 1, wherein, The method of constructing multi-dimensional constraint conditions according to adjustment power parameters of the equivalent aggregated model of flexible resources comprises: constructing flexible adjustable constraint conditions of the micro-grid group according to adjustment power parameters of the equivalent aggregated model of flexible resources; constructing power flow constraint conditions of the power distribution network based on operation data corresponding to nodes of the power distribution network; constructing system operation constraint conditions of the power distribution network by using node voltage data and branch power flow data of the system of the power distribution network; constructing distributed generator output constraint conditions of the power distribution network according to active power output data and reactive power output data of distributed generators of the power distribution network; constructing output opportunity constraint conditions of renewable energy according to uncertainty of the renewable energy; constructing multi-dimensional constraint conditions by combining the flexible adjustable constraint conditions, the power flow constraint conditions, the system operation constraint conditions, the distributed generator output constraint conditions and the output opportunity constraint conditions of the renewable energy.

4. The microgrid group and power grid collaborative optimization scheduling method according to claim 1, characterized in that, The objective function is specifically: In the formula, represent the electricity price of the wholesale market; represent the electricity purchased from the wholesale market; represent the active power of the distributed generator; , and represent the generation cost coefficient of the distributed generator; represent the node set of the distribution network; represent the unit cost of the distribution network scheduling the micro-grid group; represent the active power of the micro-grid interacting with the distribution network.

5. The microgrid cluster and power grid collaborative optimization scheduling method according to claim 3, characterized in that, The method of solving the collaborative optimization scheduling model of the micro-grid group and the power distribution network based on the linearization solving method of the Gaussian mixture model to obtain the collaborative scheduling scheme of the micro-grid group and the power distribution network comprises: linearizing the output opportunity constraint conditions of renewable energy of the collaborative optimization scheduling model of the micro-grid group and the power distribution network based on the linearization solving method of the Gaussian mixture model; solving the linearized collaborative optimization scheduling model of the micro-grid group and the power distribution network by using an optimization solver to obtain the collaborative scheduling scheme of the micro-grid group and the power distribution network.

6. The microgrid cluster and power grid collaborative optimization scheduling method according to claim 5, characterized in that, The method of linearizing the output opportunity constraint conditions of renewable energy of the collaborative optimization scheduling model of the micro-grid group and the power distribution network based on the linearization solving method of the Gaussian mixture model comprises: The output opportunity constraint condition of the renewable energy of the micro-grid group and the power distribution network collaborative optimization scheduling model is converted into a cumulative density function; The cumulative density function is standardized to obtain a prediction error value between a wind turbine and a photovoltaic generator of the renewable energy after the standardization processing; A Gaussian mixture model is used to fit the probability density function of the prediction error value between the wind turbine and the photovoltaic generator; Based on the parameters of the Gaussian mixture model, the fitted probability density function is converted into a linear combination of a plurality of Gaussian distribution cumulative density functions; The linear combination of the Gaussian distribution cumulative density functions is segmented and linearized by using a translation method and a scaling method.

7. A micro-grid group and power distribution network collaborative optimization scheduling system, characterized in that, Comprise: The acquisition module is used for acquiring the operation data corresponding to the electric vehicles and the temperature-controlled loads in the micro-grid group, and constructing energy storage models according to the operation characteristics of the operation data, and aggregating all the energy storage models to obtain an equivalent aggregation model of flexible resources; The first construction module is used for constructing a multi-dimensional constraint condition according to the adjustment power parameters of the equivalent aggregation model of flexible resources; The second construction module is used for constructing a micro-grid group and power distribution network collaborative optimization scheduling model under the multi-dimensional constraint condition, with the minimum operation cost of the power distribution network as an objective function; The solving module is used for solving the micro-grid group and power distribution network collaborative optimization scheduling model based on a linearization solving method of a Gaussian mixture model, to obtain a micro-grid group and power distribution network collaborative scheduling scheme.

8. An electronic device, comprising: The computer program is executed to realize the micro-grid group and power distribution network collaborative optimization scheduling method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to realize the micro-grid group and power distribution network collaborative optimization scheduling method according to any one of claims 1-6.

10. A computer program product, characterised in that, The computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the micro-grid group and power distribution network collaborative optimization scheduling method according to any one of claims 1-6.

Images