Microgrid energy management and control method and device

Abstract

The present invention discloses a microgrid energy management and control method and device, which relates to the field of power dispatching technology, wherein the method comprises: obtaining photovoltaic output data, electricity price data and load data; establishing a microgrid energy management and dispatching model according to a first constraint condition, photovoltaic output data, electricity price data and load data; establishing a market clearing model according to a second constraint condition, photovoltaic output data, electricity price data and load data; solving the microgrid energy management and dispatching model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a backup capacity cost ARC, and obtaining the net load demand and total backup demand of the microgrid. Thus, the convergence of the net load demand and total backup demand of the microgrid and the microgrid LMP and ARC is achieved, and finally a joint optimization result is obtained, which provides theoretical guidance for behavioral decision-making to reduce the green energy reduction ratio.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a microgrid energy management and control method and device. Background Art

[0002] In the related art, the tradable green certificate (TGC) in the existing technology is subject to unbundled sales, which limits the TGC to be sold separately from the underlying energy and can be used nationwide. In this case, although such TGCs provide a flexible method to support the development of renewable energy, they cannot change the existing electricity contracts and physical power transmission of enterprises.

[0003] The clearing price of the spot market is based on the economic dispatch in the power system. Green electricity (GE) enjoys priority dispatch rights due to its zero marginal cost. However, no matter how high the Renewable Portfolio Standard (RPS) quota ratio is set, the reduction of GE is inevitable when the GE penetration rate increases. As the GE ratio increases, the RPS mechanism may gradually become ineffective: the additional consumption of GE will greatly increase the ancillary service costs of thermal power and occupy the share of thermal power, making it impossible to further consume GE in cost-oriented economic dispatch. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, one purpose of the present invention is to propose a microgrid energy management and control method to achieve the convergence of the net load demand and total standby demand of the microgrid as well as the locational marginal price (LMP) and the Apportioned Reserve Cost (ARC) of the microgrid node, and finally obtain a joint optimization result to provide theoretical guidance for behavioral decision-making to reduce the green energy reduction ratio.

[0005] Another object of the present invention is to provide a microgrid energy management and control device.

[0006] To achieve the above object, an embodiment of the present invention provides a microgrid energy management and control method, including:

[0007] Obtain photovoltaic output data, electricity price data and load data;

[0008] Establishing a microgrid energy management and scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data;

[0009] Establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data, and the load data;

[0010] The microgrid energy management scheduling model and the market clearing model are solved by the diagonalization algorithm, the node electricity price LMP and the reserve capacity cost ARC to obtain the net load demand and the total reserve demand of the microgrid.

[0011] In addition, the microgrid energy management and control method according to the above embodiment of the present invention may also have the following additional technical features:

[0012] According to one embodiment of the present invention, the microgrid energy management scheduling model is established according to the preset first constraint condition, the photovoltaic output data, the electricity price data and the load data, including:

[0013] The objective function of establishing the microgrid energy management scheduling model is:

[0014]

[0015] in, Cost of electricity from micro-turbines; the cost of procuring tradable green certificates; transaction costs for the utility grid; The cost of the allocated spare capacity; Costs for load shifting;

[0016] The constraints for constructing the microgrid energy management and scheduling model include: a first power balance constraint, a first power supply constraint, and an energy storage system constraint;

[0017] The first power balance constraint is:

[0018]

[0019] in, represents the net load at time t in the sth scenario; represents the original load demand at time t in the sth scenario; and They represent the charging and discharging power of the energy storage system at time t in the sth scenario respectively; and They represent the upward and downward power of the transferable load at time t in the sth scenario respectively; and They represent the wind energy, micro-turbine power generation and solar energy at time t in the sth scenario respectively; represents the TGC requirement in the sth scenario; δ RPS It represents the minimum amount of electricity that electricity users must consume from renewable energy sources as a percentage of their total load as required by the Renewable Portfolio Standard (RPS);

[0020] Wherein, the first power supply constraint is:

[0021]

[0022] in, and They represent the upper limits of available power of wind and solar energy at time t in the sth scenario respectively; It represents the upper limit of the power generated by the microturbine; and They represent the upward limit power and downward limit power of the transferable load at time t respectively; Indicates the operating status of distributed energy on node x;

[0023] Wherein, the energy storage system constraints are:

[0024]

[0025] in, and They represent the limits of the charging power and discharging power of the energy storage system respectively; represents the amount of electricity stored in the energy storage system at time t in the sth scenario; and Respectively represent the charging and discharging power of the energy storage system; Indicates the capacity of the energy storage system; and They respectively represent the upper and lower limits of the energy storage system's state of charge.

[0026] According to one embodiment of the present invention, the The cost of electricity from micro-turbines The cost of purchasing tradable green certificates, The transaction costs of the utility grid, The amortized reserve capacity costs and The cost of load transfer is expressed as:

[0027]

[0028] in, and are the coefficients for the electricity cost of the microturbine and the cost of load shifting, respectively; represents the power generation of the microturbine at time t in the sth scenario; GCIndicates TGC price; represents the TGC requirement of node x at time t in the sth scenario; represents the removed power of node x in the power system at time t in the sth scenario; represents the net load of node x at time t in the sth scenario; λ fixed represents a constant export price between the microgrid and the utility grid; represents the marginal electricity price of node x at time t in the sth scenario; ΔT represents the time interval; γ s represents the probability of scene s appearing.

[0029] According to one embodiment of the present invention, the establishment of a market clearing model is:

[0030]

[0031] in, represents the conventional generator cost; represents the spare capacity cost;

[0032] The second constraint condition includes: a second power balance constraint, a line flow constraint, a second power supply constraint and an RPS constraint;

[0033] The second power balance constraint is:

[0034]

[0035] in, and They represent the power of conventional generator i and renewable energy source i at time t in the sth scenario respectively; Indicates the reserve power of conventional generators; represents the net load at time t in the sth scenario; and They represent the net load demand and the portion of electricity reserved by renewable energy sources, respectively;

[0036] The line power flow constraint is:

[0037]

[0038] Among them, G k-i represents the generation transfer allocation factor of line k; and They represent the power of conventional generator i, the power of renewable energy i and the reserve power of conventional generator i at time t in the sth scenario respectively; represents the net load at time t in the sth scenario; represents the transmission capacity of line k;

[0039] Wherein, the second power supply constraint is:

[0040]

[0041] Among them, P i G,min and P i G,max denote the minimum and maximum power of conventional generator i respectively; Indicates that the green generator can provide power; P i U,max and P i D,max denote the rising and falling limits of the conventional generator i, respectively;

[0042] The RPS constraint is:

[0043]

[0044] in, Indicates the power demand of TGC in scenario s.

[0045] According to one embodiment of the present invention, the microgrid energy management scheduling model and the market clearing model are solved by a diagonalization algorithm, a node electricity price LMP and a backup capacity cost ARC to obtain a net load demand and a total backup demand of the microgrid, including:

[0046] S1, initialize iteration index k = 0, set the operation strategy of the microgrid and the net load Set to initial value;

[0047] S2, based on the node electricity price LMP and the reserve capacity cost ARC, based on load balancing, wind power, solar energy and energy storage operation constraints and RPS requirements, the tradable green certificate (TGC) cost and operation cost of the microgrid are minimized to obtain the net load demand and total reserve demand of the microgrid;

[0048] S3, determine whether the current optimization result and LMP and ARC meet the convergence conditions with the previous one. If the convergence conditions are met, output the optimization result and LMP and ARC; if not, proceed to step S4;

[0049] S4, performing economic dispatch including RPS constraints and safety constraints according to the net load demand and the total reserve demand, adjusting the output and updating the LMP and the ARC with the goal of minimizing the regional operating cost on the premise of satisfying the constraints of the generator, renewable power generation and line flow, and returning to step S2.

[0050] According to one embodiment of the present invention, the calculation formula of the LMP is:

[0051]

[0052] Among them, λ t,s It means that the LMP in the joint settlement model can be expressed by the Lagrange multiplier to achieve;

[0053] The calculation formula of the ARC is:

[0054]

[0055] According to the microgrid energy management and control method of the embodiment of the present invention, by obtaining photovoltaic output data, electricity price data and load data; establishing a microgrid energy management scheduling model according to the first constraint condition, photovoltaic output data, electricity price data and load data; establishing a market clearing model according to the second constraint condition, photovoltaic output data, electricity price data and load data; solving the microgrid energy management scheduling model and the market clearing model through the diagonalization algorithm, the node electricity price LMP and the backup capacity cost ARC, the net load demand and total backup demand of the microgrid are obtained. Thus, the convergence of the net load demand and total backup demand of the microgrid and the microgrid LMP and ARC is achieved, and finally a joint optimization result is obtained, which provides theoretical guidance for behavioral decision-making to reduce the green energy reduction ratio.

[0056] To achieve the above object, another embodiment of the present invention proposes a microgrid energy management and control device, including: an acquisition module for acquiring photovoltaic output data, electricity price data and load data;

[0057] A first establishing module, used to establish a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data;

[0058] A second establishing module, used to establish a market clearing model according to a second constraint condition, the photovoltaic output data, the electricity price data, and the load data;

[0059] The solution module is used to solve the microgrid energy management scheduling model and the market clearing model through the diagonalization algorithm, the node electricity price LMP and the reserve capacity cost ARC to obtain the net load demand and the total reserve demand of the microgrid.

[0060] According to the microgrid energy management and control device of the embodiment of the present invention, by acquiring photovoltaic output data, electricity price data and load data; establishing a microgrid energy management scheduling model according to the first constraint condition, photovoltaic output data, electricity price data and load data; establishing a market clearing model according to the second constraint condition, photovoltaic output data, electricity price data and load data; solving the microgrid energy management scheduling model and the market clearing model through the diagonalization algorithm, the node electricity price LMP and the backup capacity cost ARC, the net load demand and total backup demand of the microgrid are obtained. Thus, the convergence of the net load demand and total backup demand of the microgrid and the microgrid LMP and ARC is achieved, and finally a joint optimization result is obtained, which provides theoretical guidance for behavioral decision-making to reduce the green energy reduction ratio.

[0061] According to a third aspect of an embodiment of the present invention, a server is provided, including:

[0062] processor;

[0063] a memory for storing instructions executable by the processor;

[0064] Among them, the processor is configured to execute the instructions to implement the microgrid energy management and control method described in the embodiment of the first aspect.

[0065] According to a fourth aspect of an embodiment of the present invention, there is provided a storage medium, including:

[0066] When the instructions in the storage medium are executed by the processor of the server, the server is enabled to execute the microgrid energy management and control method described in the first aspect of the embodiment.

[0067] According to a fifth aspect of an embodiment of the present invention, a computer program product is provided. When instructions in the computer program product are executed by a processor, a server is enabled to execute the microgrid energy management and control method described in the embodiment of the first aspect.

[0068] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of a microgrid energy management and control method according to an embodiment of the present invention;

[0070] Figure 2 is a flow chart of a microgrid energy management and control method according to another embodiment of the present invention;

[0071] Figure 3 It is a structural diagram of a microgrid energy management and control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0072] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0073] The following describes a microgrid energy management and control method and device according to an embodiment of the present invention with reference to the accompanying drawings.

[0074] The microgrid energy management and control of the present invention targets the green power market including renewable energy, introduces and analyzes the equilibrium in the spot market with RPS constraints, constructs a green power model with RPS constraints, and provides theoretical guidance for behavioral decisions to reduce the green energy reduction ratio.

[0075] Among them, the Renewable Portfolio Standard (RPS) is one of the most popular and innovative renewable energy incentives, according to which a certain percentage of the region's total annual electricity supply must come from renewable energy sources; on the other hand, the smooth implementation of RPS requires corresponding and compelling strategies as an effective tool. Therefore, as a matching strategy, tradable green certificates (TGCs) are traded and redeemed for profit, representing a certain amount of green electricity (GE), and GE producers can seek additional benefits by signing financial contracts with quota obligation bearers to sell their TGCs.

[0076] Figure 1 FIG. 1 is a flow chart of a microgrid energy management and control method according to an embodiment of the present invention. Figure 1 As shown, the microgrid energy management and control method includes the following steps:

[0077] Step S101, obtaining photovoltaic output data, electricity price data and load data.

[0078] In this embodiment, the IEEE 14 bus system is simulated to verify the proposed model. There are 3 conventional generators and 2 100MW wind farms in the model. 5 microgrids equipped with transferable loads and distributed energy devices are located at 5 different nodes. The proportion of transferable loads in each microgrid is close to 20% of the initial load each time. Each microgrid has solar energy, micro gas turbines, wind turbines and energy storage, with capacities of 10MW, 35MW, 40MW and 20MW respectively. According to the load and solar energy data in 2016, 10 typical scenarios are generated using K-means, and the probability of each scenario occurring is 0.1. For example, in a certain scenario, the solar energy, micro gas turbine, wind turbine and energy storage power generation of the microgrid are 7MW, 25MW, 36MW and 12MW respectively.

[0079] Step S102: establishing a microgrid energy management and dispatching model according to the first constraint condition, photovoltaic output data, electricity price data and load data.

[0080] In this embodiment, the objective function of establishing the microgrid energy management scheduling model is:

[0081]

[0082] in, Cost of electricity from micro-turbines; the cost of procuring tradable green certificates; transaction costs for the utility grid; The cost of the allocated spare capacity; Costs for load shifting;

[0083] The constraints for constructing the microgrid energy management and scheduling model include: a first power balance constraint, a first power supply constraint, and an energy storage system constraint;

[0084] The first power balance constraint is:

[0085]

[0086] in, represents the net load at time t in the sth scenario; represents the original load demand at time t in the sth scenario; and They represent the charging and discharging power of the energy storage system at time t in the sth scenario respectively; and They represent the upward and downward power of the transferable load at time t in the sth scenario respectively; and They represent the wind energy, micro-turbine power generation and solar energy at time t in the sth scenario respectively; represents the TGC requirement in the sth scenario; δ RPS It represents the minimum amount of electricity that electricity users must consume from renewable energy sources as a percentage of their total load as required by the Renewable Portfolio Standard (RPS);

[0087] Among them, the first power constraint is:

[0088]

[0089] in, and They represent the upper limits of available power of wind and solar energy at time t in the sth scenario respectively; It represents the upper limit of the power generated by the microturbine; and They represent the upward limit power and downward limit power of the transferable load at time t respectively; Indicates the operating status of distributed energy on node x;

[0090] Wherein, the energy storage system constraints are:

[0091]

[0092] in, and They represent the limits of the charging power and discharging power of the energy storage system respectively; represents the amount of electricity stored in the energy storage system at time t in the sth scenario; and Respectively represent the charging and discharging power of the energy storage system; Indicates the capacity of the energy storage system; and They respectively represent the upper and lower limits of the energy storage system's state of charge.

[0093] In this embodiment, The cost of electricity from micro-turbines The cost of purchasing tradable green certificates, The transaction costs of the utility grid, The amortized reserve capacity costs and The cost of load transfer is expressed as:

[0094]

[0095] in, and are the coefficients for the electricity cost of the microturbine and the cost of load shifting, respectively; represents the power generation of the microturbine at time t in the sth scenario; GC Indicates TGC price; represents the TGC requirement of node x at time t in the sth scenario; represents the removed power of node x in the power system at time t in the sth scenario; represents the net load of node x at time t in the sth scenario; λ fixed represents a constant export price between the microgrid and the utility grid; represents the marginal electricity price of node x at time t in the sth scenario; ΔT represents the time interval; γ s represents the probability of scene s appearing.

[0096] Among them, the node marginal electricity price and spare capacity costs It is affected by the operation strategy of the microgrid and given by the market clearing model at the lower level taking into account the RPS constraints.

[0097] Step S103, establishing a market clearing model according to the second constraint condition, photovoltaic output data, electricity price data, and load data.

[0098] In this embodiment, the market clearing model is established as follows:

[0099]

[0100] in, represents the conventional generator cost; represents the spare capacity cost;

[0101] The second constraint condition includes: a second power balance constraint, a line flow constraint, a second power supply constraint and an RPS constraint;

[0102] The second power balance constraint is:

[0103]

[0104] in, and They represent the power of conventional generator i and renewable energy source i at time t in the sth scenario respectively; Indicates the reserve power of conventional generators; represents the net load at time t in the sth scenario; and They represent the net load demand and the portion of electricity reserved by renewable energy sources, respectively;

[0105] The line power flow constraint is:

[0106]

[0107] Among them, G k-i represents the generation transfer allocation factor of line k; and They represent the power of conventional generator i, the power of renewable energy i and the reserve power of conventional generator i at time t in the sth scenario respectively; represents the net load at time t in the sth scenario; represents the transmission capacity of line k;

[0108] Wherein, the second power supply constraint is:

[0109]

[0110] Among them, P i G,min and P i G,max denote the minimum and maximum power of conventional generator i respectively; Indicates that the green generator can provide power; P i U,max and P i D,max denote the rising and falling limits of the conventional generator i, respectively;

[0111] The RPS constraint is:

[0112]

[0113] in, Indicates the power demand of TGC in scenario s.

[0114] Step S104, solving the microgrid energy management scheduling model and the market clearing model through the diagonalization algorithm, the node electricity price LMP and the reserve capacity cost ARC, and obtaining the net load demand and the total reserve demand of the microgrid.

[0115] In this embodiment, S1, initialize the iteration index k=0, set the operation strategy of the microgrid and the net load Set to the initial value; S2, based on the node electricity price LMP and the backup capacity cost ARC, based on load balance, wind power, solar energy and energy storage operation constraints and RPS requirements, optimize with the goal of minimizing the TGC cost and operation cost of the microgrid, and obtain the net load demand and total backup demand of the microgrid; S3, determine whether the current optimization results and LMP and ARC meet the convergence conditions with the previous ones. If the convergence conditions are met, output the optimization results and LMP and ARC; if the convergence conditions are not met, proceed to step S4; S4, according to the net load demand and total backup demand, perform economic dispatch including RPS constraints and safety constraints, and adjust the output and update LMP and ARC with the goal of minimizing regional operating costs on the premise of satisfying the constraints of generators, renewable power generation and line flow, and return to step S2, that is, Figure 2 shown.

[0116] The net load demand represents the import and export power of the public grid at this node; the total reserve demand represents the need to purchase TGC covering a certain number of REs when the demand is positive.

[0117] In this embodiment, the calculation formula of LMP is:

[0118]

[0119] Among them, λ t,s It means that the LMP in the joint settlement model can be expressed by the Lagrange multiplier to achieve;

[0120] The calculation formula of the ARC is:

[0121]

[0122] In this embodiment, the regional reserve requirement is the linear sum of the regional load and the required reserve capacity of green power, and the regional marginal reserve price is the Lagrange multiplier λ t，s , and the reserve cost is allocated by the percentage of each microgrid in the total net load.

[0123] Therefore, the market equilibrium in the RPS-constrained spot market is introduced and analyzed, and a green power regulation technology model with RPS constraints is constructed. Through iterative interaction between independent system operators and microgrids, the convergence of the microgrid's net load demand and total reserve demand as well as the microgrid's LMP and ARC are achieved, and finally the joint optimization result is obtained, which provides theoretical guidance for behavioral decisions to reduce the green energy reduction ratio.

[0124] According to the microgrid energy management and control method proposed in the embodiment of the present invention, by obtaining photovoltaic output data, electricity price data and load data; establishing a microgrid energy management scheduling model according to the first constraint condition, photovoltaic output data, electricity price data and load data; establishing a market clearing model according to the second constraint condition, photovoltaic output data, electricity price data and load data; solving the microgrid energy management scheduling model and the market clearing model through the diagonalization algorithm, the node electricity price LMP and the backup capacity cost ARC, the net load demand and total backup demand of the microgrid are obtained. Thus, the convergence of the net load demand and total backup demand of the microgrid and the microgrid LMP and ARC is achieved, and finally a joint optimization result is obtained, which provides theoretical guidance for behavioral decision-making to reduce the green energy reduction ratio.

[0125] Figure 3 : is a structural example diagram of a microgrid energy management and control device according to an embodiment of the present invention. Figure 3 As shown, the microgrid energy management and control device includes: an acquisition module 100, a first establishment module 200, a second establishment module 300 and a solution module 400.

[0126] The acquisition module 100 is used to acquire photovoltaic output data, electricity price data and load data.

[0127] The first establishing module 200 is used to establish a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data.

[0128] The second establishing module 300 is used to establish a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data, and the load data.

[0129] The solution module 400 is used to solve the microgrid energy management scheduling model and the market clearing model through the diagonalization algorithm, the node electricity price LMP and the reserve capacity cost ARC to obtain the net load demand and the total reserve demand of the microgrid.

[0130] It should be noted that the aforementioned explanation of the embodiment of the microgrid energy management and control method is also applicable to the microgrid energy management and control device of this embodiment, and will not be repeated here.

[0131] According to the microgrid energy management and control device proposed in the embodiment of the present invention, by acquiring photovoltaic output data, electricity price data and load data; establishing a microgrid energy management scheduling model according to the first constraint condition, photovoltaic output data, electricity price data and load data; establishing a market clearing model according to the second constraint condition, photovoltaic output data, electricity price data and load data; solving the microgrid energy management scheduling model and the market clearing model through the diagonalization algorithm, the node electricity price LMP and the backup capacity cost ARC, the net load demand and total backup demand of the microgrid are obtained. Thus, the convergence of the net load demand and total backup demand of the microgrid and the microgrid LMP and ARC is achieved, and finally a joint optimization result is obtained, which provides theoretical guidance for behavioral decision-making to reduce the green energy reduction ratio.

[0132] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0133] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0134] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0135] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A microgrid energy management and control method, characterized in that: include: Obtain photovoltaic output data, electricity price data and load data; Establishing a microgrid energy management and scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data; Establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data, and the load data; The microgrid energy management scheduling model and the market clearing model are solved by a diagonalization algorithm, a node electricity price LMP and a reserve capacity cost ARC to obtain a net load demand and a total reserve demand of the microgrid; The establishing of a microgrid energy management and dispatching model according to the preset first constraint condition, the photovoltaic output data, the electricity price data and the load data comprises: The objective function of establishing the microgrid energy management scheduling model is: in, Cost of electricity from micro-turbines; the cost of procuring tradable green certificates; transaction costs for the utility grid; The cost of the allocated spare capacity; Costs for load shifting; The constraints for constructing the microgrid energy management and scheduling model include: a first power balance constraint, a first power supply constraint, and an energy storage system constraint; The first power balance constraint is: in, represents the net load at time t in the sth scenario; represents the original load demand at time t in the sth scenario; and They represent the charging and discharging power of the energy storage system at time t in the sth scenario respectively; and They represent the upward and downward power of the transferable load at time t in the sth scenario respectively; and They represent the wind energy, micro-turbine power generation and solar energy at time t in the sth scenario respectively; represents the demand for tradable green certificates TGC in the sth scenario; δ RPS It represents the minimum amount of electricity that electricity users must consume from renewable energy sources as a percentage of their total load as required by the Renewable Portfolio Standard (RPS); Wherein, the first power supply constraint is: in, and They represent the upper limits of available power of wind and solar energy at time t in the sth scenario respectively; It represents the upper limit of the power generated by the microturbine; and They represent the upward limit power and downward limit power of the transferable load at time t respectively; Indicates the operating status of distributed energy on node x; Wherein, the energy storage system constraints are: in, and They represent the limits of the charging power and discharging power of the energy storage system respectively; represents the amount of electricity stored in the energy storage system at time t in the sth scenario; and Respectively represent the charging and discharging power of the energy storage system; Indicates the capacity of the energy storage system; and They represent the upper and lower limits of the energy storage system’s state of charge respectively; The market clearing model is established as follows: in, represents the conventional generator cost; represents the spare capacity cost; The second constraint condition includes: a second power balance constraint, a line flow constraint, a second power supply constraint and an RPS constraint; The second power balance constraint is: in, and They represent the power of conventional generator i and renewable energy source i at time t in the sth scenario respectively; Indicates the reserve power of conventional generators; represents the net load at time t in the sth scenario; and They represent the net load demand and the portion of electricity reserved by renewable energy sources, respectively; The line power flow constraint is: Among them, G k-i represents the generation transfer allocation factor of line k; and They represent the power of conventional generator i, the power of renewable energy i and the reserve power of conventional generator i at time t in the sth scenario respectively; represents the net load at time t in the sth scenario; represents the transmission capacity of line k; Wherein, the second power supply constraint is: Among them, P i G,min and P i G,max denote the minimum and maximum power of conventional generator i respectively; Indicates that the green generator can provide power; P i U,max and P i D,max denote the rising and falling limits of the conventional generator i, respectively; The RPS constraint is: in, Represents the electricity demand of tradable green certificates TGC under scenario s.

2. The microgrid energy management and control method according to claim 1, characterized in that: Said The cost of electricity from micro-turbines The cost of purchasing tradable green certificates, The transaction costs of the utility grid, The amortized reserve capacity costs and The cost of load transfer is expressed as: in, and are the coefficients for the electricity cost of the microturbine and the cost of load shifting, respectively; represents the power generation of the microturbine at time t in the sth scenario; GC Indicates the price of tradable green certificates TGC; represents the tradable green certificate TGC demand of node x at time t in the sth scenario; represents the removed power of node x in the power system at time t in the sth scenario; represents the net load of node x at time t in the sth scenario; λ fixed represents a constant export price between the microgrid and the utility grid; represents the marginal electricity price of node x at time t in the sth scenario; ΔT represents the time interval; γ s represents the probability of scene s appearing.

3. The microgrid energy management and control method according to claim 1, characterized in that: The microgrid energy management scheduling model and the market clearing model are solved by the diagonalization algorithm, the node electricity price LMP and the reserve capacity cost ARC to obtain the net load demand and the total reserve demand of the microgrid, including: S1, initialize iteration index k = 0, set the operation strategy of the microgrid and the net load Set to initial value; S2, based on the node electricity price LMP and the reserve capacity cost ARC, based on load balancing, wind power, solar energy and energy storage operation constraints and RPS requirements, the tradable green certificate (TGC) cost and operation cost of the microgrid are minimized to obtain the net load demand and total reserve demand of the microgrid; S3, judging whether the current optimization result and LMP and ARC meet the convergence condition with the previous one, if the convergence condition is met, outputting the optimization result and LMP and ARC; if the convergence condition is not met, proceeding to step S4; S4, performing economic dispatch including RPS constraints and safety constraints according to the net load demand and the total reserve demand, adjusting the output and updating the LMP and the ARC with the goal of minimizing the regional operating cost on the premise of satisfying the constraints of the generator, renewable power generation and line flow, and returning to step S2.

4. The microgrid energy management and control method according to claim 1, characterized in that: The calculation formula of the LMP is: Among them, λ t,s The LMP in the joint settlement model can be expressed by the Lagrange multiplier to achieve; The calculation formula of the ARC is:

5. A microgrid energy management and control device, characterized in that: include: Acquisition module, used to obtain photovoltaic output data, electricity price data and load data; A first establishing module, used to establish a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data; A second establishing module, used to establish a market clearing model according to a second constraint condition, the photovoltaic output data, the electricity price data, and the load data; A solution module is used to solve the microgrid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a reserve capacity cost ARC to obtain a net load demand and a total reserve demand of the microgrid; The first establishing module is used to: The objective function of establishing the microgrid energy management scheduling model is: in, Cost of electricity from micro-turbines; the cost of procuring tradable green certificates; transaction costs for the utility grid; The cost of the allocated spare capacity; Costs for load shifting; The constraints for constructing the microgrid energy management and scheduling model include: a first power balance constraint, a first power supply constraint, and an energy storage system constraint; The first power balance constraint is: in, represents the net load at time t in the sth scenario; represents the original load demand at time t in the sth scenario; and They represent the charging and discharging power of the energy storage system at time t in the sth scenario respectively; and They represent the upward and downward power of the transferable load at time t in the sth scenario respectively; and They represent the wind energy, micro-turbine power generation and solar energy at time t in the sth scenario respectively; represents the demand for tradable green certificates TGC in the sth scenario; δ RPS It represents the minimum amount of electricity that electricity users must consume from renewable energy sources as a percentage of their total load as required by the Renewable Portfolio Standard (RPS); Wherein, the first power supply constraint is: in, and They represent the upper limits of available power of wind and solar energy at time t in the sth scenario respectively; It represents the upper limit of the power generated by the microturbine; and They represent the upward limit power and downward limit power of the transferable load at time t respectively; Indicates the operating status of distributed energy on node x; Wherein, the energy storage system constraints are: in, and They represent the limits of the charging power and discharging power of the energy storage system respectively; represents the amount of electricity stored in the energy storage system at time t in the sth scenario; and Respectively represent the charging and discharging power of the energy storage system; Indicates the capacity of the energy storage system; and They represent the upper and lower limits of the energy storage system’s state of charge respectively; The market clearing model is established as follows: in, represents the conventional generator cost; represents the spare capacity cost; The second constraint condition includes: a second power balance constraint, a line flow constraint, a second power supply constraint and an RPS constraint; The second power balance constraint is: in, and They represent the power of conventional generator i and renewable energy source i at time t in the sth scenario respectively; Indicates the reserve power of conventional generators; represents the net load at time t in the sth scenario; and They represent the net load demand and the portion of electricity reserved by renewable energy sources, respectively; The line power flow constraint is: Among them, G k-i represents the generation transfer allocation factor of line k; and They represent the power of conventional generator i, the power of renewable energy i and the reserve power of conventional generator i at time t in the sth scenario respectively; represents the net load at time t in the sth scenario; represents the transmission capacity of line k; Wherein, the second power supply constraint is: Among them, P i G,min and P i G,max denote the minimum and maximum power of conventional generator i respectively; Indicates that the green generator can provide power; P i U,max and P i D,max denote the rising and falling limits of the conventional generator i, respectively; The RPS constraint is: in, Represents the electricity demand of tradable green certificates TGC under scenario s.

6. The microgrid energy management and control device according to claim 5, characterized in that: Said The cost of electricity from micro-turbines The cost of purchasing tradable green certificates, The transaction costs of the utility grid, The amortized reserve capacity costs and The cost of load transfer is expressed as: in, and are the coefficients for the electricity cost of the microturbine and the cost of load shifting, respectively; represents the power generation of the microturbine at time t in the sth scenario; GC Indicates the price of tradable green certificates TGC; represents the tradable green certificate TGC demand of node x at time t in the sth scenario; represents the removed power of node x in the power system at time t in the sth scenario; represents the net load of node x at time t in the sth scenario; λ fixed represents a constant export price between the microgrid and the utility grid; represents the marginal electricity price of node x at time t in the sth scenario; ΔT represents the time interval; γ s represents the probability of scene s appearing.

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