CN112651112B - Collaborative decision-making method, system and equipment for electric energy transaction and system operation of internet micro-grid - Google Patents

Collaborative decision-making method, system and equipment for electric energy transaction and system operation of internet micro-grid Download PDF

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CN112651112B
CN112651112B CN202011498365.3A CN202011498365A CN112651112B CN 112651112 B CN112651112 B CN 112651112B CN 202011498365 A CN202011498365 A CN 202011498365A CN 112651112 B CN112651112 B CN 112651112B
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micro
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CN112651112A (en
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李佳勇
余轶
许道森
海征
周斌
张聪
黎灿兵
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Hunan University
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/10—Flexible AC transmission systems [FACTS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

The invention provides a collaborative decision method, a collaborative decision system and collaborative decision equipment for electric energy transaction and system operation of an interconnection micro-grid, which comprise the following steps: based on a given P2P transaction electricity price, taking the minimum running cost of each micro-grid as an objective function, and establishing a data driving distribution robust optimization model of a single micro-grid; according to a data driving distribution robust optimization model of a single micro-grid, a plurality of flexible interconnection micro-grid energy management models are established by taking the minimum total running cost of all micro-grids as an objective function; and converting the multi-microgrid energy management model into a linear programming model, and carrying out the cooperation decision of the electric energy transaction and the system operation of the internet microgrid through the linear programming model. The invention can process three-phase asymmetric operation of the micro-grid and grid operation constraint, establish a distributed robust optimization model of the independent micro-grid, and optimize the operation cost under the condition of given P2P trading electricity price. And a novel decentralization electricity price mechanism is designed for P2P electric energy transaction to determine the P2P transaction electricity price reflecting the market value.

Description

Collaborative decision-making method, system and equipment for electric energy transaction and system operation of internet micro-grid
Technical Field
The invention relates to the field of distributed optimal scheduling of power systems, in particular to a collaborative decision-making method, a collaborative decision-making system and collaborative decision-making equipment for electric energy transaction and system operation of an interconnection micro-grid.
Background
Interconnected micro-grids (or micro-grids) are generally scheduled and operated in a centralized manner by power distribution network operators, and along with the construction and development of the micro-grids, the micro-grids are often affiliated to different owners, and each micro-grid has own operation modes and management policies. In order to protect the privacy of the micro-grid and ensure the operation autonomy of the micro-grid, the scheduling management cannot be carried out through a centralized method, and the research on the cooperative decision-making method of the P2P electric energy transaction and the system operation of the interconnected micro-grid is necessary. Typically, the micro-grids are interconnected by incorporating local distribution networks, but this has drawbacks. First, if the connected lines of the micro-grids fail, the micro-grids will be disconnected. Second, since the transfer of power follows kirchhoff's law, the transfer power between interconnected micro-grids is not fully controllable. There are many studies on interconnected micro-grid power trading at present, however, these studies only focus on the design of power trading mechanisms, and do not consider the collaborative optimization of P2P power trading and grid operation; to simplify the problem, many studies reduce the independent micro-grid to one node, without considering the internal structure and operation constraints of the micro-grid, nor the conditions of three-phase asymmetric operation. Current research usually deals with uncertainty by random optimization, which requires an accurate probability distribution function, which is not usually available in practice, or by robust optimization, which is only directed to the worst implementation scenario of uncertainty, with the result being too conservative.
Disclosure of Invention
The embodiment of the invention provides an interconnection micro-grid electric energy transaction and system operation collaborative decision-making method, which can process three-phase asymmetric operation and grid operation constraint of a micro-grid, establishes a distributed robust optimization model of an independent micro-grid and optimizes the operation cost under the condition of giving P2P transaction electricity price; a novel decentralization electricity price mechanism is designed for P2P electric energy transaction to determine the P2P transaction electricity price reflecting the market value.
In a first aspect, an embodiment of the present invention provides a method for collaborative decision-making of electric energy transaction and system operation in an interconnected micro-grid, where the micro-grids are interconnected by a flexible switching device, including the following steps:
based on a given P2P transaction electricity price, taking the minimum operation cost of each micro-grid as an objective function, establishing a data driving distribution robust optimization model of a single micro-grid, wherein the data driving distribution robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy power generation, operation constraints of a battery energy storage system, operation constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation;
According to the data driving distribution robust optimization model of the single micro-grid, a plurality of flexibly interconnected multi-micro-grid energy management models are established by taking the minimum total running cost of all micro-grids as an objective function;
and performing decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing internet microgrid electric energy transaction and system operation collaborative decision through the linear programming model.
Optionally, the operation cost includes an operation cost related to the uncertainty and an operation cost unrelated to the uncertainty.
Optionally, the power flow model of the three-phase asymmetric power grid is a linearized three-phase line power flow model, and the power flow model of the three-phase asymmetric power grid includes: an active power model of a single micro-grid, a reactive power model of a single micro-grid, and node voltage constraints in the micro-grid.
Optionally, the operation constraint of the distributed controllable generator and the new energy power generation includes: reserve power constraints of the distributed controllable generator, active power constraints of the distributed controllable generator, reactive power constraints of the distributed controllable generator, three-phase imbalance constraints and reactive power constraints of the new energy generator.
Optionally, the operation constraint of the battery energy storage system includes: the method comprises the steps of discharging power constraint of a battery energy storage system, charging power constraint of the battery energy storage system, standby power constraint of the battery energy storage system, allowable power constraint of the battery energy storage system and state of charge constraint of the battery energy storage system.
Optionally, the flexible switching device operation constraint includes: the power balance of each phase of the flexible switching device and the switching power constraint of the flexible switching device.
Optionally, the distributed controllable generator and battery energy storage system real-time power adjustment strategy for uncertain power deviation includes: affine rules for power adjustment of the distributed controllable generator, affine rules for power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
Optionally, the step of performing the decentralizing pricing calculation on the multi-microgrid energy management model and converting the multi-microgrid energy management model into the linear programming model specifically includes:
performing decentralization pricing calculation on the multi-microgrid energy management model by an alternate direction multiplier method;
the multi-microgrid energy management model is converted to a linear programming model by a fuzzy set-based wasperstein metric.
In a second aspect, an embodiment of the present invention further provides an interconnected micro-grid power transaction and system operation collaborative decision-making system, where the micro-grids are interconnected by a flexible switching device, where the system includes:
the system comprises a first establishing module, a second establishing module and a third establishing module, wherein the first establishing module is used for establishing a data-driven distributed robust optimization model of a single micro-grid based on a given P2P transaction electricity price by taking the minimum running cost of each micro-grid as an objective function, and the data-driven distributed robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, running constraints of a distributed controllable generator and new energy power generation, running constraints of a battery energy storage system, running constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation;
the second building module is used for building a plurality of flexibly-interconnected multi-micro-network energy management models by taking the minimum total running cost of all micro-networks as an objective function according to the data driving distribution robust optimization model of the single micro-network;
the conversion module is used for carrying out decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and carrying out cooperative decision-making of internet microgrid electric energy transaction and system operation through the linear programming model.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: the system comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps in the method for collaborative decision-making of the internet P2P electric energy transaction and the system operation provided by the embodiment of the invention when the processor executes the computer program.
In the embodiment of the invention, based on a given P2P transaction electricity price, a data driving distribution robust optimization model of a single micro-grid is established by taking the minimum operation cost of each micro-grid as an objective function, wherein the data driving distribution robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy power generation, operation constraints of a battery energy storage system, operation constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation; according to the data driving distribution robust optimization model of the single micro-grid, a plurality of flexibly interconnected multi-micro-grid energy management models are established by taking the minimum total running cost of all micro-grids as an objective function; and performing decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing internet microgrid electric energy transaction and system operation collaborative decision through the linear programming model. The three-phase asymmetric operation and the grid operation constraint of the micro grid can be processed, a distributed robust optimization model of the independent micro grid is established, and the operation cost of the independent micro grid is optimized under the condition of given P2P trading electricity price; a novel decentralization electricity price mechanism is designed for P2P electric energy transaction to determine the P2P transaction electricity price reflecting the market value.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a collaborative decision-making method for internet micro-grid power transaction and system operation provided in an embodiment of the present invention;
fig. 1a is a schematic diagram of a connection between two interconnected three-phase asymmetric micro-nets according to an embodiment of the present invention;
FIG. 1b is a schematic diagram of a collaborative optimization method framework provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of energy purchasing of a micro-grid 1 according to an embodiment of the present invention;
fig. 3 is a schematic diagram of energy purchasing of a micro-grid 2 according to an embodiment of the present invention;
fig. 4 is a schematic diagram of energy purchasing of a micro-grid 3 according to an embodiment of the present invention;
fig. 5 is a schematic diagram of energy purchasing of a micro-grid 4 according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a transaction electricity price per hour provided by an embodiment of the present invention;
Fig. 7 is a schematic diagram of social cost of different methods under different sample sizes according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the invention, a decentralization pricing scheme is provided, and the P2P transaction electric energy and electricity price among multiple micro networks can be determined by utilizing an alternate direction multiplier Algorithm (ADMM), so that the privacy and autonomy of different micro network bodies are protected. The invention considers the working condition of three-phase asymmetric operation of the micro-grid and is tightly combined with the actual engineering problem. Meanwhile, flexible interconnection of a plurality of micro-grids is realized by using flexible switching equipment (SOP), so that complete controllability of power transmission among the micro-grids is realized; when a certain micro-grid fails, the fault can be quickly isolated due to direct current connection in the SOP. In addition, the cooperative optimization decision of P2P electric energy transaction and system operation is considered, the uncertainty of the load and the new energy is processed by adopting a distributed robust optimization method, and the defects that the requirement of random optimization on the accurate probability distribution of variables is too high and the robust optimization result is too conservative are overcome.
Referring to fig. 1, fig. 1 is a flowchart of a collaborative decision-making method for internet micro-grid power transaction and system operation, which is provided in an embodiment of the present invention, as shown in fig. 1, and includes the following steps:
101. based on a given P2P transaction electricity price, a data driving distribution robust optimization model of a single micro-grid is established by taking the minimum running cost of each micro-grid as an objective function.
In the embodiment of the invention, the data-driven distributed robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy power generation, operation constraints of a battery energy storage system, operation constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation.
Further, referring to fig. 1a and fig. 1b, fig. 1a is a schematic connection diagram of two interconnected three-phase asymmetric micro-networks according to an embodiment of the present invention, and fig. 1b is a schematic frame diagram of a collaborative optimization method according to an embodiment of the present invention. Fig. 1a shows an example in which two three-phase asymmetrically operated micro-grids are connected to each other by means of an SOP consisting of two back-to-back voltage source inverters. The power flow transmitted through the SOP is completely controllable, which lays a technical foundation for P2P electric energy transaction between interconnected micro-grids. In order to promote P2P power trading and ensure operational safety and reliability, a data-driven Distributed Robust Optimization (DRO) based collaborative optimization model of independent microgrid power trading and grid operation is proposed, and the general description of the proposed solution is shown in fig. 1 b. Each microgrid may purchase electrical energy from the main grid at a given price or may be acquired from other microgrids at a contract price in the form of a P2P transaction. In addition, since new energy generation and load demand are uncertain in the early stages of the day, the DRO method is adopted to deal with uncertainty. Each microgrid schedules its DG and BESS to minimize operating costs while reserving standby power during the early days for real-time power adjustments during the real-time phase using designed affine rules.
Further, the power flow model of the three-phase asymmetric power grid is a linearized three-phase line power flow model, and the power flow model of the three-phase asymmetric power grid includes: an active power model of a single micro-grid, a reactive power model of a single micro-grid, and node voltage constraints in the micro-grid.
In the embodiment of the invention, a linearized three-phase line power flow (DistFlow) model is used for describing the power flow in the three-phase asymmetric micro-network, and the method can be specifically described as follows:
Figure BDA0002842869400000061
Figure BDA0002842869400000062
Figure BDA0002842869400000063
Figure BDA0002842869400000064
Figure BDA0002842869400000065
wherein, in the above formulas (1 a) to (1 e),
Figure BDA0002842869400000066
is the active output of the distributed controllable generator g,/->
Figure BDA0002842869400000067
Reactive output of the distributed controllable generator g; />
Figure BDA0002842869400000068
Is the active power on line i in micro-net m,/>
Figure BDA0002842869400000069
Is the reactive power on line i in micro-grid m; />
Figure BDA00028428694000000610
Is the active power flow transmitted from the micro-grid n to the micro-grid m; />
Figure BDA00028428694000000611
Is the discharge power of the energy storage system b, +.>
Figure BDA00028428694000000612
Is the charging power of the energy storage system b; />
Figure BDA00028428694000000613
Is the mean square value of the voltage of the node i in the micro-grid m; />
Figure BDA00028428694000000614
Is the active load of node i, +.>
Figure BDA00028428694000000615
Is the reactive load of node i; />
Figure BDA00028428694000000616
Binary parameters indicating whether node i is installed with a distributed controllable generator g (e.g., 0 indicates no installation, 1 indicates installation), binary parameters indicating whether node i is installed with a new energy generator k (e.g., 0 indicates no installation, 1 indicates installation), and the like >
Figure BDA00028428694000000617
A binary parameter indicating whether node i has a battery energy storage system b installed (e.g., 0 for no installation, 1 for installation); />
Figure BDA00028428694000000618
Indicating whether a terminal of the SOP connecting the micro-grids m and n is connected with the bus i; a, a max Is the maximum tap ratio of the voltage regulator, a min Is the minimum tap ratio of the voltage regulator. Wherein the formulas (1 a) and (1 b) represent the active and reactive power balance at each node; equation (1 c) indicates that the mean square value of the voltage at the secondary side node of the voltage regulator should not exceed its adjustable range, where ≡c indicates multiplication of corresponding elements; equation (1 d) describes the voltage drop across the distribution line, wherein +.>
Figure BDA00028428694000000619
Is the equivalent line impedance, is a 3x3 complex matrix; equation (1 e) represents the upper and lower limits of the node voltage.
According to the tidal current model of the three-phase asymmetric power grid,
Figure BDA00028428694000000620
indicating the active power exchange of the micro network m and the main network at time t. Thus, the cost (profit) of the micro network m to purchase (sell) energy from the main network at time t can be expressed as:
Figure BDA00028428694000000621
in the formula (2)
Figure BDA0002842869400000071
Representing the price of buying (selling) energy from the main network, usually +.>
Figure BDA0002842869400000072
Above->
Figure BDA0002842869400000073
(.) + represents the projection operation (x) into a non-negative quadrant + ＝max(x,0)。
Likewise, the cost (profit) of the microgrid m to conduct P2P power transactions with other microgrids at time t may be expressed as:
Figure BDA0002842869400000074
In the formula (3)
Figure BDA0002842869400000075
Representing the P2P power trade price between micro-nets m and n.
In the embodiment of the invention, the operation constraint of the distributed controllable generator and the new energy power generation comprises: reserve power constraints of the distributed controllable generator, active power constraints of the distributed controllable generator, reactive power constraints of the distributed controllable generator, three-phase imbalance constraints and reactive power constraints of the new energy generator. Specifically, the above-described distributed controllable generator (DG) operating constraints are represented as follows:
Figure BDA0002842869400000076
Figure BDA0002842869400000077
Figure BDA0002842869400000078
Figure BDA0002842869400000079
in the formulas (4 a) to (4 d),
Figure BDA00028428694000000710
representing the upward standby power in a distributed controllable generator g in phi%>
Figure BDA00028428694000000711
Representing the downward standby power in the distributed controllable generator g in phi phase; />
Figure BDA00028428694000000712
Representing the upper limit of reactive output of the distributed controllable generator g in phi phase, +.>
Figure BDA00028428694000000713
The lower limit of reactive power output of the distributed controllable generator g in phi phase is indicated.
Wherein equation (4 a) represents that the active force of each phase of DG is within a range in which reserves are considered; equation (4 b) illustrates that the reserve value is non-negative; equation (4 c) indicates that the reactive power output per phase of DG is within an allowable range; equation (4 d) shows that DG has a three-phase imbalance of not more than the maximum allowable value delta g 。
Optionally, in order to fully utilize the new energy to generate power, the active power output of the RG is controlled at a maximum power point, and the reactive power output of the RG is regulated within the allowable range, as shown in the following formula:
Figure BDA0002842869400000081
In the formula (5)
Figure BDA0002842869400000082
Representing reactive power of new energy generator gUpper limit of->
Figure BDA0002842869400000083
The lower limit of reactive power of the new energy generator g.
In an embodiment of the present invention, the operation constraint of the battery energy storage system includes: the method comprises the steps of discharging power constraint of a battery energy storage system, charging power constraint of the battery energy storage system, standby power constraint of the battery energy storage system, allowable power constraint of the battery energy storage system and state of charge constraint of the battery energy storage system. Specifically, each battery energy storage system BESS
Figure BDA0002842869400000084
The operating constraints of (2) are as follows:
Figure BDA0002842869400000085
Figure BDA0002842869400000086
Figure BDA0002842869400000087
Figure BDA0002842869400000088
Figure BDA0002842869400000089
Figure BDA00028428694000000810
E b,T ＝E b,0 (6g)
in the formulae (6 a) to (6 g), the formulae (6 a) and (6 b) charge and discharge BESS for each phaseLimiting the range of electric power and taking into account the stored charge-discharge reserve power, wherein
Figure BDA00028428694000000811
And->
Figure BDA00028428694000000812
Respectively representing the maximum charging power and the maximum discharging power; equations (6 c) and (6 d) ensure that the standby power is not negative, while the maximum allowable power cannot be exceeded; equation (6 e) represents the law of change in the state of charge (SOC) of the BESS; equation (6 f) represents the allowable range of SOC; equation (6 g) indicates that the SOC at the end of the day is equal to the SOC at the beginning.
Alternatively, to avoid excessive use of the BESS, the battery life penalty cost is added to the objective function, expressed as a linear function related to charge and discharge power:
Figure BDA0002842869400000091
In the formula (7), θ b Is a cost factor associated with the life-time penalty of the BESS.
In an embodiment of the present invention, the operation constraint of the flexible switching device includes: the power balance of each phase of the flexible switching device and the switching power constraint of the flexible switching device. Alternatively, to save the capacity of the SOP for active power transfer and reduce power loss, the generation or absorption of reactive power with the SOP is not considered. Specifically, the above-described flexible switching device operation constraint may be expressed as follows:
Figure BDA0002842869400000092
Figure BDA0002842869400000093
Figure BDA0002842869400000094
in the formulas (8 a) to (8 c), the formula (8 a) represents the power balance of each phase of the SOP,
Figure BDA0002842869400000095
representing the power loss of the phi phase of the SOP on the m side of the micro-grid; equation (8 b) shows that the power loss in SOP is a linear function of the switching power, where +.>
Figure BDA0002842869400000096
Is a very small loss factor (e.g., 0.02); equation (8 c) indicates that the switching power does not exceed the SOP capacity
Figure BDA0002842869400000097
In an embodiment of the present invention, the above-mentioned real-time power adjustment strategy for a distributed controllable generator and a battery energy storage system for uncertain power deviation includes: affine rules for power adjustment of the distributed controllable generator, affine rules for power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
Further, after defining the fluctuations of new energy and load, the reserve capacity of DG and BESS will be used to keep the power balance of each micro-grid. The adjustment strategy in the embodiment of the invention can be shown in the following formula:
Figure BDA0002842869400000098
Figure BDA0002842869400000099
Figure BDA00028428694000000910
Figure BDA0002842869400000101
Figure BDA0002842869400000102
Figure BDA0002842869400000103
Figure BDA0002842869400000104
in the formulae (9 a) to (9 g),
Figure BDA0002842869400000105
affine factor representing the phase phi of the distributed controllable generator g +_>
Figure BDA0002842869400000106
Representing the affine factor of the energy storage system b at phase phi. Formulas (9 a) and (9 b) represent affine rules for DG and BESS power adjustment, wherein +.>
Figure BDA0002842869400000107
Is a random variable representing the power deviation of phi phase in the micro-grid m; formulas (9 c) to (9 e) are constraints that the affine factor needs to satisfy; the formulas (9 f) and (9 g) indicate that the power adjustment of DG and BESS does not exceed the power reserve range.
It should be noted that the formulas (9 f) and (9 g) can be processed by a robust optimization method, but this results in excessively conservative results. In fact, the lack of up-power reserve and the lack of down-power reserve result in load shedding and new energy power curtailment, respectively. Further, in the embodiment of the present invention, the problem can be solved by punishing the excess. Further, the penalty function at time t for the power reserve excess for the phi phase can be expressed as:
Figure BDA0002842869400000108
In the formula (10), the above
Figure BDA0002842869400000109
Is the unit power cost of load shedding, +.>
Figure BDA00028428694000001010
The unit power cost of new energy output is reduced.
From affine adjustment strategies, the power generation cost of DG is related to the uncertainty of load and new energy output. To reduce computational complexity, the generation cost is expressed in terms of piecewise linear functions, and the total generation cost is expressed in terms of random variables
Figure BDA00028428694000001011
Is shown in the following equation:
Figure BDA00028428694000001012
in the formula (11), the number of the groups,
Figure BDA00028428694000001013
Figure BDA00028428694000001014
and->
Figure BDA00028428694000001015
Is the cost factor of the kappa segment; />
Figure BDA00028428694000001016
Figure BDA00028428694000001017
And->
Figure BDA00028428694000001018
Optionally, to quantify the cost of the BESS deviating from the intended charge-discharge plan, a BESS schedule deviation cost is constructed that is a linear function of the power deviation, as shown in the following equation:
Figure BDA0002842869400000111
in the formula (12), the number of the groups,
Figure BDA0002842869400000112
δ b,t the unit power deviation cost of BESS b at time t;
Figure BDA0002842869400000113
and->
Figure BDA0002842869400000114
In the embodiment of the invention, can be used
Figure BDA0002842869400000115
The total cost associated with uncertainty at time t, i.e., the sum of DG power generation cost, BESS schedule offset cost, and penalty cost beyond the power reserve range, can be expressed specifically as follows:
Figure BDA0002842869400000116
in the formula (13), the amino acid sequence of the formula (13),
Figure BDA0002842869400000117
is the set of decision variables for the micro-net m at time t.
In the embodiment of the invention, the aim of each micro-net is to minimize the total running cost, namely, the minimum running cost of each micro-net is taken as an objective function, a data driving distribution robust optimization model of a single micro-net is established, and the running cost is composed of two parts, wherein one part is the cost related to uncertainty, and the other part is the cost unrelated to uncertainty. Wherein the costs associated with uncertainty can be expressed in terms of its expected value at the least desirable probability density distribution. Specifically, in the embodiment of the present invention, the data-driven distributed robust optimization model of a single micro-network may be as follows:
Figure BDA0002842869400000118
Figure BDA0002842869400000119
Figure BDA00028428694000001110
In the equations (14 a) and (14 b), the equation (14 a) is an objective function, and the equation (14 b) is a constraint condition. The constraint condition (14 b) is represented by the formulas (1), (4) to (6), (8 c), and (9 a) to (9 e). Here, the expression (1) may be understood as a generic term of the expressions (1 a) to (1 e), the expression (4) may be understood as a generic term of the expressions (4 a) to (4 d), and the expression (6) may be understood as a generic term of the expressions (6 a) to (6 g). In the formulas (14 a) to (14 b),
Figure BDA00028428694000001111
102. and according to the data-driven distribution robust optimization model of the single micro-grid, establishing a plurality of flexibly interconnected multi-micro-grid energy management models by taking the minimum total running cost of all micro-grids as an objective function.
In the embodiment of the invention, the goal of the multi-microgrid energy management model is to minimize the total social cost. The total social cost can be added up by the running cost of each micro-net. Alternatively, since the P2P power transaction is an internal behavior between multiple micro networks, the cost and benefit of this part cancel each other out in the objective function. The multi-microgrid energy management model described above may be represented by the following equation:
Figure BDA0002842869400000121
Figure BDA0002842869400000122
Figure BDA0002842869400000123
in the formulae (15 a) to (15 c)
Figure BDA0002842869400000124
Since the power loss of the SOP is much smaller than the power transmitted by the SOP, the power loss in the SOP can be ignored, thereby simplifying equation (8 a) into equation (15 c).
In the embodiment of the invention, a pricing algorithm is deduced, and auxiliary variables are introduced
Figure BDA0002842869400000125
And converting (15 c) equivalently to the following form:
Figure BDA0002842869400000126
Figure BDA0002842869400000127
103. and performing decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing cooperative decision-making of internet microgrid electric energy transaction and system operation through the linear programming model.
In the embodiment of the invention, the multicenter energy management model can be subjected to decentralization pricing calculation by an alternate direction multiplier method; the multi-microgrid energy management model is converted to a linear programming model by a fuzzy set-based wasperstein metric.
In an embodiment of the invention, the Lagrangian multiplier (dual variable) associated with constraint (15 c) represents the shadow price of the P2P power transaction. Thus, an ADMM-based decentralised pricing algorithm can be employed to
Figure BDA0002842869400000128
Representing a set of auxiliary variables, i.e. +.>
Figure BDA0002842869400000129
Figure BDA00028428694000001210
Is the Lagrangian multiplier corresponding to constraint (16 b) to form an augmented Lagrangian function as follows:
Figure BDA0002842869400000131
in formula (17):
Figure BDA0002842869400000132
Figure BDA0002842869400000133
And +.>
Figure BDA0002842869400000134
In the embodiment of the invention, the iteration times can be represented by τ, and the iteration process of the above decentralizing pricing algorithm is as follows:
first, update x m : since the augmented Lagrangian function and constraints (15 b) have a decomposable formal structure, each independent micro-net can autonomously update its own associated x m A variable. Fixing the auxiliary variable obtained from the last iteration
Figure BDA0002842869400000135
And Lagrangian multiplier->
Figure BDA0002842869400000136
Then optimizing Lagrangian function, micro-grid m can realize the variable +.>
Figure BDA0002842869400000137
The specific updates may be as shown in the following examples:
Figure BDA0002842869400000138
after solving equation (18), the micro-net m will be able to
Figure BDA0002842869400000139
Is transmitted to the micro-net n.
Then update y m : the auxiliary variable y is updated by solving the following equation:
Figure BDA00028428694000001310
s.t.(16a) (19b)
wherein the expression (19 a) is a solution target, the expression (19 b) is a constraint condition, and the constraint condition is the expression (16 a).
By mathematical operations, the following analytical solutions can be derived:
Figure BDA00028428694000001311
as can be seen from equation (20), each independent micro-net can autonomously update its own related variable y m . By receiving from the micro-network n
Figure BDA00028428694000001312
And->
Figure BDA00028428694000001313
The micro-grid m is updated according to the formula (20)>
Figure BDA00028428694000001314
Finally update lambda m : the update may be performed according to the following equation:
Figure BDA00028428694000001315
in equation (21), the micro-net m updates the Lagrangian multiplier locally and then will
Figure BDA0002842869400000141
Is transmitted to the micro-net n.
From the formula (20) and the formula (21)
Figure BDA0002842869400000142
When the consistency constraint (16 b) is satisfied, equation (18) is equivalent to equation (14), which means that the convergence solution achieves Nash equalization.
Alternatively, the DRO (multi-microgrid energy management model) equation (15) can be converted into a linear programming problem by using the wasperstein metric, so that the convergence of the ADMM algorithm is ensured.
It should be noted that in practical applications, the probability distribution of random variables is generally unknown, and there is only one set of history samples
Figure BDA0002842869400000143
May be used. The embodiment of the invention can construct the probability distribution ambiguity set (WM) through Wasserstein Measurement (WM)>
Figure BDA0002842869400000144
Thus, when data outside the sample set is used, the simulation effect is not too biased; ensuring convergence, namely converging the fuzzy set to real probability distribution when the number of samples tends to infinity; the original unsolvable problem can be equivalently converted into a resolvable optimization problem, which is convenientAnd (5) calculating.
Specifically, given a set of historical samples, according to a probability distribution
Figure BDA0002842869400000145
An estimated P value can be obtained, wherein +.>
Figure BDA0002842869400000146
Representation->
Figure BDA0002842869400000147
Dirac measure of (a). WM is a method describing the "distance" between the estimated distribution and the true distribution, defined as:
Figure BDA0002842869400000148
in equation (22), n is a tightly-supported set of random variables, n is
Figure BDA0002842869400000149
And->
Figure BDA00028428694000001410
Edge distribution w and->
Figure BDA00028428694000001411
Is a joint distribution of (a). Further, the fuzzy set structure is shown in the following formula:
Figure BDA00028428694000001412
in equation (13), ε (N) is the fuzzy set
Figure BDA00028428694000001413
Is +.>
Figure BDA00028428694000001414
It is a function of the confidence level beta and the number of samples N, in particularThis can be expressed by the following formula:
Figure BDA00028428694000001415
in the equation (24), D is a constant representing the support diameter of the random variable.
Further, to reduce the computational complexity, the cost expectation in the worst case of the probability distribution is approximated by an upper-limit function, as follows:
Figure BDA0002842869400000151
in equation (25), the first two parts on the right represent the worst case expected value of the power generation cost and the expected value of the BESS scheduling offset cost, respectively, and the last part is the worst case expected value of the penalty cost that is out of the power reserve range. According to equations (11) and (12), the worst case expected values of the power generation cost and the BESS scheduling offset cost can be summarized as:
Figure BDA0002842869400000152
let w be represented as a polyhedron, for example: xi = { w: cw.ltoreq.d }; equation (26) can be converted into:
Figure BDA0002842869400000153
/>
Figure BDA0002842869400000154
Figure BDA0002842869400000155
γ jκ ≥0 (27d)
the above formulae (27 a) to (27 d) may be collectively referred to as the formula (27), and the above formula (27) is about x and { μ, s ] j ,γ jk Linear programming problem.
Figure BDA0002842869400000156
Can be equivalently rewritten as:
Figure BDA0002842869400000157
Figure BDA0002842869400000158
Due to
Figure BDA0002842869400000159
Is about w φ Point-by-point determination of the linear function set of (2) with respect to w φ Is a convex function of (a). To the left of equation (28 b) is maximizing a convex function over a compact interval; the maximum point is included inW φ ，/>
Figure BDA0002842869400000161
And->
Figure BDA0002842869400000162
Among the three. Thus, formula (28) may be further converted into:
Figure BDA0002842869400000163
Figure BDA0002842869400000164
Figure BDA0002842869400000165
Figure BDA0002842869400000166
formula (29) relates to x and
Figure BDA0002842869400000167
is a linear programming problem.
Thus, the equation (15) and the equation (18) can be converted into a linear programming model. And carrying out the electric energy transaction of the interconnection micro-grid and the collaborative decision of the system operation through a linear programming model.
In the embodiment of the invention, based on a given P2P transaction electricity price, taking the minimum running cost of each micro-grid as an objective function, establishing a data driving distribution robust optimization model of a single micro-grid, wherein the data driving distribution robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, running constraints of a distributed controllable generator and new energy power generation, running constraints of a battery energy storage system, running constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation; according to the data driving distribution robust optimization model of the single micro-grid, a multi-micro-grid energy management model is established by taking the minimum total running cost of all micro-grids as an objective function; and performing decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing internet microgrid electric energy transaction and system operation collaborative decision through the linear programming model. The three-phase asymmetric operation and the grid operation constraint of the micro grid can be processed, a distributed robust optimization model of the independent micro grid is established, and the operation cost of the independent micro grid is optimized under the condition of given P2P trading electricity price; a novel decentralization electricity price mechanism is designed for P2P electric energy transaction to determine the P2P transaction electricity price reflecting the market value.
To further illustrate the effects of the embodiments of the present invention, the embodiments of the present invention were tested with a multi-microgrid system constructed from IEEE 123 node data, which consisted of 4 three-phase asymmetrically operated microgrids. The effectiveness of the invention is verified through simulation analysis.
In particular, table 1 shows a comparison of microgrid cost or revenue for a multi-microgrid system using P2P power transactions versus without P2P power transactions.
Table 1 cost and benefit of microgrid power trading
Figure BDA0002842869400000171
As shown in Table 1, the operation cost of the micro-grid is reduced by applying the cooperative decision-making method of the electric energy transaction and the system operation of the Internet, the total electric energy acquisition cost is reduced by 20%, and the effectiveness of the P2P electric energy transaction scheme and the decentralization pricing method provided by the patent is verified.
Referring to fig. 2 to 5, fig. 2 is a schematic diagram of energy purchasing of a micro-grid 1 according to an embodiment of the present invention, fig. 3 is a schematic diagram of energy purchasing of a micro-grid 2 according to an embodiment of the present invention, fig. 4 is a schematic diagram of energy purchasing of a micro-grid 3 according to an embodiment of the present invention, fig. 5 is a schematic diagram of energy purchasing of a micro-grid 4 according to an embodiment of the present invention, as shown in fig. 2 to 5, surplus electricity of the micro-grid 1 is sold to the micro-grid 4, surplus electricity of the micro-grid 3 is mostly sold to the micro-grid 2, and the rest is sold to the micro-grid 4. At 10-15 points, the photovoltaic output is higher, the electric energy generated by the micro-grid can meet the demands of all loads and buyers, and as can be seen from the figure, the micro-grid is more prone to trading the electric energy with other micro-grids through P2P trading.
Referring to fig. 6, fig. 6 is a schematic diagram of a transaction electricity price per hour according to an embodiment of the present invention. In FIG. 6, lambda b Representing the price of electricity purchased from a mains network, lambda s Indicating the price of electricity for selling electric energy to a main network, lambda ij And the P2P transaction electricity price of the micro-grid i and the micro-grid j is represented. As can be seen from FIG. 6, the P2P trading electricity price is at lambda s And lambda (lambda) b In between, both transaction parties can benefit by participating in P2P transaction, and the electricity price mechanism provided by the patent has an excitation effect.
Referring to fig. 7, fig. 7 is a schematic diagram of social costs of different methods under different sample sizes according to an embodiment of the present invention. As shown in fig. 7, in case of different sample sizes, optimal social costs are obtained using Robust Optimization (RO), distributed Robust Optimization (DRO), and random optimization (SP), respectively. As can be seen from the figure, the RO process is too conservative and the cost is highest. The SP method underestimates the actual cost and results in the lowest cost. The DRO approach yields a cost between RO and SP that represents the worst-case cost in a given fuzzy set. As the sample increases gradually, the cost of the DRO method decreases gradually.
Table 2 lists the intra-sample cost and the extra-sample cost for different sized sample sets, solved and compared using random optimization (SP) and Distributed Robust Optimization (DRO), respectively.
TABLE 2 comparison of SP versus DRO for sample sets of different sizes
Figure BDA0002842869400000181
As can be seen from table 2, for the sample size tested, the expected cost inside the sample using the SP method is approximated by the average of the expected cost outside the sample, which is small and has a large deviation from the actual. As the sample size increases, the bias under the SP method gradually decreases. In contrast, the intra-sample cost using the DRO method is an upper limit of the extra-sample cost, and as the sample size increases, both the intra-sample cost and the extra-sample average value using the DRO method have a decreasing trend.
Table 3 lists the in-sample cost and out-of-sample mean values obtained using the DRO, deterministic algorithm (DET) and RO methods.
TABLE 3 DRO, DET vs. RO algorithm
Figure BDA0002842869400000182
From table 3, it can be seen that the cost is highest inside the sample using the RO method, whereas the mean outside the sample is highest using the DET method since DET does not take uncertainty into account. In contrast, using the DRO method, which yields the lowest intra-sample cost and out-of-sample average, may reduce costs by describing the uncertainty. Thus, DRO overcomes the problem of RO being too conservative while being more robust than SP.
It should be noted that, the method for collaborative decision-making of internet micro-grid electric energy transaction and system operation provided by the embodiment of the invention can be applied to devices such as mobile phones, monitors, computers, servers and the like which can perform collaborative decision-making of internet micro-grid electric energy transaction and system operation.
Optionally, the device for implementing the collaborative decision-making system for internet micro-grid electric energy transaction and system operation provided by the embodiment of the invention includes:
the system comprises a first establishing module, a second establishing module and a third establishing module, wherein the first establishing module is used for establishing a data-driven distributed robust optimization model of a single micro-grid based on a given P2P transaction electricity price by taking the minimum running cost of each micro-grid as an objective function, and the data-driven distributed robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, running constraints of a distributed controllable generator and new energy power generation, running constraints of a battery energy storage system, running constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation;
the second building module is used for building a plurality of flexibly-interconnected multi-micro-network energy management models by taking the minimum total running cost of all micro-networks as an objective function according to the data driving distribution robust optimization model of the single micro-network;
the conversion module is used for carrying out decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and carrying out cooperative decision-making of internet microgrid electric energy transaction and system operation through the linear programming model.
Optionally, the operation cost includes an operation cost related to the uncertainty and an operation cost unrelated to the uncertainty.
Optionally, the power flow model of the three-phase asymmetric power grid is a linearized three-phase line power flow model, and the power flow model of the three-phase asymmetric power grid includes: an active power model of a single micro-grid, a reactive power model of a single micro-grid, and node voltage constraints in the micro-grid.
Optionally, the operation constraint of the distributed controllable generator and the new energy power generation includes: reserve power constraints of the distributed controllable generator, active power constraints of the distributed controllable generator, reactive power constraints of the distributed controllable generator, three-phase imbalance constraints and reactive power constraints of the new energy generator.
Optionally, the operation constraint of the battery energy storage system includes: the method comprises the steps of discharging power constraint of a battery energy storage system, charging power constraint of the battery energy storage system, standby power constraint of the battery energy storage system, allowable power constraint of the battery energy storage system and state of charge constraint of the battery energy storage system.
Optionally, the flexible switching device operation constraint includes: the power balance of each phase of the flexible switching device and the switching power constraint of the flexible switching device.
Optionally, the distributed controllable generator and battery energy storage system real-time power adjustment strategy for uncertain power deviation includes: affine rules for power adjustment of the distributed controllable generator, affine rules for power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
Optionally, the conversion module includes:
the decentralized pricing calculation unit is used for performing decentralized pricing calculation on the multi-microgrid energy management model through an alternate direction multiplier method;
and the linear programming conversion unit is used for converting the multi-microgrid energy management model into a linear programming model through Wasserstein measurement based on fuzzy sets.
It should be noted that the collaborative decision-making system for internet micro-grid electric energy transaction and system operation provided by the embodiment of the invention can be applied to devices such as a mobile phone, a monitor, a computer, a server and the like which can perform collaborative decision-making for internet micro-grid electric energy transaction and system operation.
The system for collaborative decision-making of the internet micro-grid electric energy transaction and the system operation provided by the embodiment of the invention can realize each process realized by the method for collaborative decision-making of the internet micro-grid electric energy transaction and the system operation in the embodiment of the method, and can achieve the same beneficial effects. In order to avoid repetition, a description thereof is omitted.
Optionally, an electronic device provided by an embodiment of the present invention includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
the processor is used for calling the computer program stored in the memory and executing the following steps:
based on a given P2P transaction electricity price, taking the minimum operation cost of each micro-grid as an objective function, establishing a data driving distribution robust optimization model of a single micro-grid, wherein the data driving distribution robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy power generation, operation constraints of a battery energy storage system, operation constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation;
according to the data driving distribution robust optimization model of the single micro-grid, a plurality of flexibly interconnected multi-micro-grid energy management models are established by taking the minimum total running cost of all micro-grids as an objective function;
and performing decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing internet microgrid electric energy transaction and system operation collaborative decision through the linear programming model.
Optionally, the operation cost includes an operation cost related to the uncertainty and an operation cost unrelated to the uncertainty.
Optionally, the power flow model of the three-phase asymmetric power grid is a linearized three-phase line power flow model, and the power flow model of the three-phase asymmetric power grid includes: an active power model of a single micro-grid, a reactive power model of a single micro-grid, and node voltage constraints in the micro-grid.
Optionally, the operation constraint of the distributed controllable generator and the new energy power generation includes: reserve power constraints of the distributed controllable generator, active power constraints of the distributed controllable generator, reactive power constraints of the distributed controllable generator, three-phase imbalance constraints and reactive power constraints of the new energy generator.
Optionally, the operation constraint of the battery energy storage system includes: the method comprises the steps of discharging power constraint of a battery energy storage system, charging power constraint of the battery energy storage system, standby power constraint of the battery energy storage system, allowable power constraint of the battery energy storage system and state of charge constraint of the battery energy storage system.
Optionally, the flexible switching device operation constraint includes: the power balance of each phase of the flexible switching device and the switching power constraint of the flexible switching device.
Optionally, the distributed controllable generator and battery energy storage system real-time power adjustment strategy for uncertain power deviation includes: affine rules for power adjustment of the distributed controllable generator, affine rules for power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
Optionally, the step of performing, by the processor, the decentralizing pricing calculation on the multi-microgrid energy management model, and converting the multi-microgrid energy management model into the linear programming model specifically includes:
performing decentralization pricing calculation on the multi-microgrid energy management model by an alternate direction multiplier method;
the multi-microgrid energy management model is converted to a linear programming model by a fuzzy set-based wasperstein metric.
It should be noted that the electronic device may be a mobile phone, a monitor, a computer, a server, etc. that may be used to make a collaborative decision for the electric energy transaction and the system operation of the internet.
The electronic device provided by the embodiment of the invention can realize each process realized by the collaborative decision method for the electric energy transaction and the system operation of the internet in the embodiment of the method, can achieve the same beneficial effects, and is not repeated here for avoiding repetition.
The embodiment of the invention also provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the computer program realizes each process of the collaborative decision method for the internet micro-grid electric energy transaction and the system operation provided by the embodiment of the invention, and can achieve the same technical effect, and in order to avoid repetition, the repeated description is omitted.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (RandomAccess Memory, RAM) or the like.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.

Claims (9)

1. The method for cooperatively deciding the electric energy transaction and the system operation of the interconnection micro-grid is characterized in that the micro-grids are interconnected through flexible switching equipment, and the method comprises the following steps:
Based on a given P2P transaction electricity price, taking the minimum operation cost of each micro-grid as an objective function, establishing a data driving distribution robust optimization model of a single micro-grid, wherein the data driving distribution robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy power generation, operation constraints of a battery energy storage system, operation constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation;
according to the data driving distribution robust optimization model of the single micro-grid, a plurality of flexibly interconnected multi-micro-grid energy management models are established by taking the minimum total running cost of all micro-grids as an objective function;
performing decentralization pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing internet microgrid electric energy transaction and system operation collaborative decision-making through the linear programming model; the method specifically comprises the following steps:
performing decentralization pricing calculation on the multi-microgrid energy management model by an alternate direction multiplier method;
converting the multi-microgrid energy management model into a linear programming model through a Wasserstein metric based on fuzzy sets;
Wherein the multi-microgrid energy management model is represented by the following formula:
Figure FDA0004264306740000011
Figure FDA0004264306740000012
Figure FDA0004264306740000013
in the formulas (15 a) to (15 c),
Figure FDA0004264306740000014
the formula (1) in the formula (15 b) is a linearized three-phase line power flow model, and specifically includes:
Figure FDA0004264306740000015
Figure FDA0004264306740000016
Figure FDA0004264306740000021
Figure FDA0004264306740000022
Figure FDA0004264306740000023
wherein, in the above formulas (1 a) to (1 e),
Figure FDA0004264306740000024
is the active output of the distributed controllable generator g,/->
Figure FDA0004264306740000025
Reactive output of the distributed controllable generator g; />
Figure FDA0004264306740000026
Is the active power on line i in micro-net m,/>
Figure FDA0004264306740000027
Is the reactive power on line i in micro-grid m; />
Figure FDA0004264306740000028
Is the active power flow transmitted from the micro-grid n to the micro-grid m; />
Figure FDA0004264306740000029
Is the discharge power of the energy storage system b, +.>
Figure FDA00042643067400000210
Is a storage deviceCharging power of the system b; />
Figure FDA00042643067400000211
Is the mean square value of the voltage of the node i in the micro-grid m; />
Figure FDA00042643067400000212
Is the active load of node i, +.>
Figure FDA00042643067400000213
Is the reactive load of node i; />
Figure FDA00042643067400000214
Binary parameters indicating whether node i is equipped with a distributed controllable generator g, +.>
Figure FDA00042643067400000215
Binary parameters indicating whether node i is equipped with a new energy generator k, ++>
Figure FDA00042643067400000216
A binary parameter indicating whether node i has a battery energy storage system b installed; />
Figure FDA00042643067400000217
Indicating whether a terminal of the SOP connecting the micro-grids m and n is connected with the bus i; a, a max Is the maximum tap ratio of the voltage regulator, a min Is the minimum tap ratio of the voltage regulator; wherein the formulas (1 a) and (1 b) represent the active and reactive power balance at each node; equation (1 c) indicates that the mean square value of the voltage at the secondary side node of the voltage regulator should not exceed its adjustable range, where ≡c indicates multiplication of corresponding elements; equation (1 d) describes the voltage drop across the distribution line, wherein +. >
Figure FDA00042643067400000218
Is the equivalent line impedance, is a 3x3 complex matrix; formula (1)e) Representing upper and lower limits of the node voltage;
equation (4) described in equation (15 b) is a distributed controllable generator DG operating constraint, comprising:
Figure FDA00042643067400000219
Figure FDA00042643067400000220
Figure FDA00042643067400000221
Figure FDA00042643067400000222
in the formulas (4 a) to (4 d),
Figure FDA00042643067400000223
representing the upward standby power in the distributed controllable generator g in phi phase,
Figure FDA00042643067400000224
representing the downward standby power in the distributed controllable generator g in phi phase; />
Figure FDA00042643067400000225
Representing the upper limit of reactive output of the distributed controllable generator g in phi phase, +.>
Figure FDA00042643067400000226
Representing a lower limit of reactive power output of the distributed controllable generator g in phi phase; wherein equation (4 a) represents that the active force of each phase of DG is within a range in which reserves are considered; equation (4 b) indicates that the reserve value is non-negative; the seed is4c) Indicating that the reactive power output of each phase of DG is within an allowable range; equation (4 d) shows that DG has a three-phase imbalance of not more than the maximum allowable value delta g ；
Equation (6) described in equation (15 b) is the battery energy storage system BESS, and the relevant operating constraints include, for all battery energy storage systems:
Figure FDA0004264306740000031
Figure FDA0004264306740000032
Figure FDA0004264306740000033
Figure FDA0004264306740000034
Figure FDA0004264306740000035
Figure FDA0004264306740000036
Eb,T＝Eb,0(6g)
of the formulae (6 a) to (6 g), the formulae (6 a) and (6 b) limit the range of BESS charge/discharge power of each phase and take into consideration the charge/discharge reserve power of the stored energy, wherein
Figure FDA0004264306740000037
And->
Figure FDA0004264306740000038
Respectively representing the maximum charging power and the maximum discharging power; equations (6 c) and (6 d) ensure that the standby power is not negative, while the maximum allowable power cannot be exceeded; equation (6 e) represents the law of change in the state of charge SOC of the BESS; equation (6 f) represents the allowable range of SOC; equation (6 g) indicates that the SOC at the end of the day is equal to the SOC at the beginning;
equation (8 c) described in equation (15 b) is a flexible switchgear operation constraint, equation (8) specifically including:
Figure FDA0004264306740000039
Figure FDA00042643067400000310
Figure FDA00042643067400000311
in the formulas (8 a) to (8 c), the formula (8 a) represents the power balance of each phase of the SOP,
Figure FDA0004264306740000041
representing the power loss of the phi phase of the SOP on the m side of the micro-grid; equation (8 b) shows that the power loss in SOP is a linear function of the switching power, where +.>
Figure FDA0004264306740000042
Is a very small loss factor such as 0.02; equation (8 c) indicates that the switching power does not exceed SOP capacity->
Figure FDA0004264306740000043
Equation (9) described in equation (15 b) is a distributed controllable generator and battery energy storage system real-time power adjustment strategy for uncertain power deviation, specifically comprising:
Figure FDA0004264306740000044
Figure FDA0004264306740000045
Figure FDA0004264306740000046
Figure FDA0004264306740000047
Figure FDA0004264306740000048
in the formulas (9 a) to (9 e),
Figure FDA0004264306740000049
affine factor representing the phase phi of the distributed controllable generator g +_>
Figure FDA00042643067400000410
The affine factor representing the phase phi of the energy storage system b, equation (9 a) and equation (9 b) represent affine rules for DG and BESS power adjustment, wherein +. >
Figure FDA00042643067400000411
Is a random variable representing the power deviation of phi phase in the micro-grid m; the formulas (9 c) to (9 e) are constraints that the affine factor needs to satisfy.
2. The interconnected micro-grid power transaction and system operational collaborative decision-making method according to claim 1, wherein the operational costs include an operational cost associated with uncertainty and an operational cost unrelated to uncertainty.
3. The method for collaborative decision-making of internet micro-grid power trading and system operation according to claim 1, wherein the power flow model of the three-phase asymmetric power grid is a linearized three-phase line power flow model, and the power flow model of the three-phase asymmetric power grid comprises: an active power model of a single micro-grid, a reactive power model of a single micro-grid, and node voltage constraints in the micro-grid.
4. The interconnected micro-grid power trading and system operation collaborative decision-making method according to claim 1, wherein the operational constraints of the distributed controllable generator and the new energy generation include: reserve power constraints of the distributed controllable generator, active power constraints of the distributed controllable generator, reactive power constraints of the distributed controllable generator, three-phase imbalance constraints and reactive power constraints of the new energy generator.
5. The interconnected micro-grid power transaction and system operational collaborative decision-making method of claim 1, wherein operational constraints of the battery energy storage system include: the method comprises the steps of discharging power constraint of a battery energy storage system, charging power constraint of the battery energy storage system, standby power constraint of the battery energy storage system, allowable power constraint of the battery energy storage system and state of charge constraint of the battery energy storage system.
6. The interconnected micro-grid power transaction and system operational collaborative decision-making method of claim 1, wherein the flexible switching device operational constraints comprise: the power balance of each phase of the flexible switching device and the switching power constraint of the flexible switching device.
7. The interconnected micro-grid power trading and system operating collaborative decision-making method according to claim 1, wherein the distributed controllable generator and battery energy storage system real-time power adjustment strategy for uncertain power bias comprises: affine rules for power adjustment of the distributed controllable generator, affine rules for power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
8. An interconnected micro-grid power trading and system operation collaborative decision-making system for implementing the steps in the interconnected micro-grid power trading and system operation collaborative decision-making method according to any one of claims 1-7, wherein the micro-grids are interconnected by a flexible switching device, the system comprising:
The system comprises a first establishing module, a second establishing module and a third establishing module, wherein the first establishing module is used for establishing a data-driven distributed robust optimization model of a single micro-grid based on a given P2P transaction electricity price by taking the minimum running cost of each micro-grid as an objective function, and the data-driven distributed robust optimization model of the single micro-grid comprises a tide model of a three-phase asymmetric power grid, running constraints of a distributed controllable generator and new energy power generation, running constraints of a battery energy storage system, running constraints of flexible switch equipment and a real-time power adjustment strategy of the distributed controllable generator and the battery energy storage system aiming at uncertain power deviation;
the second building module is used for building a plurality of flexibly-interconnected multi-micro-network energy management models by taking the minimum total running cost of all micro-networks as an objective function according to the data driving distribution robust optimization model of the single micro-network;
the conversion module is used for carrying out decentralization pricing calculation on the multi-microgrid energy management model through an alternate direction multiplier method, converting the multi-microgrid energy management model into a linear programming model through Wasserstein measurement based on fuzzy set, and carrying out cooperative decision on internet microgrid electric energy transaction and system operation through the linear programming model;
Wherein the multi-microgrid energy management model is the energy management model of claim 1.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the interconnected micro-grid power transaction and system operation collaborative decision-making method of any one of claims 1-7 when the computer program is executed.
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