CN112651112A - Interconnected micro-grid electric energy transaction and system operation cooperative decision method, system and equipment - Google Patents

Interconnected micro-grid electric energy transaction and system operation cooperative decision method, system and equipment Download PDF

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CN112651112A
CN112651112A CN202011498365.3A CN202011498365A CN112651112A CN 112651112 A CN112651112 A CN 112651112A CN 202011498365 A CN202011498365 A CN 202011498365A CN 112651112 A CN112651112 A CN 112651112A
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microgrid
power
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CN112651112B (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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Abstract

The invention provides a method, a system and equipment for making a collaborative decision on the electric energy transaction and system operation of an interconnected microgrid, wherein the method comprises the following steps: based on a given P2P trading electricity price, establishing a data-driven distribution robust optimization model of a single microgrid by taking the minimum operation cost of each microgrid as an objective function; the method comprises the steps that a distributed robust optimization model is driven according to data of a single micro-grid, and a plurality of flexible interconnected micro-grid energy management models are established by taking the minimum total operation cost of all micro-grids as a target function; and converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model. The method can process three-phase asymmetric operation of the micro-grid and power grid operation constraint, establish a distribution robust optimization model of the independent micro-grid, and optimize the operation cost of the micro-grid under the condition of giving P2P trading electricity price. A novel decentralized electricity price mechanism is designed for P2P electric energy trading to determine P2P trading electricity price reflecting market value.

Description

Interconnected micro-grid electric energy transaction and system operation cooperative decision method, system and equipment
Technical Field
The invention relates to the field of distributed optimal scheduling of power systems, in particular to a collaborative decision method, a collaborative decision system and a collaborative decision device for electric energy transaction and system operation of an interconnected microgrid.
Background
An interconnected microgrid (or referred to as a microgrid) is generally dispatched and operated in a centralized manner by a power distribution network operator, and along with the construction and development of the microgrid, the microgrid is usually owned by different owners, and each microgrid has an own operation mode and management policy. In order to protect the privacy of the microgrid and guarantee the operation autonomy of the microgrid, the dispatching management cannot be carried out by a centralized method, and it is necessary to research a cooperative decision method for the power trading and system operation of the interconnected microgrid P2P. Usually, the micro-grid is interconnected through a power distribution network incorporated into the local area, but this has disadvantages. Firstly, if the connected line of the micro-grids fails, the micro-grids are disconnected. Secondly, the transmission power between interconnected micro-grids is not fully controllable, since the transmission of power follows kirchhoff's law. At present, many researches on the electric energy transaction of the interconnected micro-grid exist, but the researches only focus on the design of an electric energy transaction mechanism and do not consider the cooperative optimization of the P2P electric energy transaction and the operation of the grid; in order to simplify the problem, many researches simplify an independent microgrid into a node, and do not consider the internal structure and operation constraints of the microgrid and the condition of three-phase asymmetric operation. Current research usually deals with uncertainty with methods of stochastic or robust optimization, which however requires an accurate probability distribution function, which is usually not available in practice, whereas robust optimization only implements scenarios for the worst of uncertainty, with overly conservative results.
Disclosure of Invention
The embodiment of the invention provides an interconnected microgrid electric energy transaction and system operation cooperative decision method, which can process three-phase asymmetric operation and power grid operation constraint of a microgrid, establish a distributed robust optimization model of an independent microgrid and optimize the operation cost of the microgrid under the condition of giving P2P transaction electricity price; a novel decentralized electricity price mechanism is designed for P2P electric energy trading to determine P2P trading electricity price reflecting market value.
In a first aspect, an embodiment of the present invention provides a cooperative decision method for electric energy transaction and system operation of interconnected micro-grids, where the micro-grids are interconnected through flexible switch devices, including the following steps:
based on a given P2P trading electricity price, establishing a data-driven distribution robust optimization model of a single microgrid by taking the minimum operation cost of each microgrid as an objective function, wherein the data-driven distribution robust optimization model of the single microgrid comprises a power flow model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy 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;
driving a distributed robust optimization model according to the data of the single microgrid, and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as a target function;
and performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model.
Optionally, the operating cost includes uncertainty-related operating cost and uncertainty-independent operating cost.
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 microgrid, a reactive power model of the single microgrid and node voltage constraint in the microgrid.
Optionally, the operation constraints of the distributed controllable generator and the new energy power generation include: the method comprises the following steps of standby power constraint of the distributed controllable generator, active output constraint of the distributed controllable generator, reactive output constraint of the distributed controllable generator, three-phase unbalance constraint and reactive output constraint of the new energy generator.
Optionally, the operation constraint of the battery energy storage system includes: the method comprises the following steps of discharge power constraint of a battery energy storage system, charge 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 charge state constraint of the battery energy storage system.
Optionally, the flexible switchgear operation constraints include: power balancing of each phase of the flexible switching device and switching power constraints of the flexible switching device.
Optionally, the strategy for adjusting the real-time power of the distributed controllable generator and the battery energy storage system according to the uncertain power deviation includes: affine rules of power adjustment of the distributed controllable generator, affine rules of power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
Optionally, the step of performing decentralized pricing calculation on the multi-microgrid energy management model and converting the multi-microgrid energy management model into a linear programming model specifically includes:
performing decentralized pricing calculation on the multi-microgrid energy management model through an alternative direction multiplier method;
converting the multi-microgrid energy management model into a linear programming model through fuzzy set-based Wasserstein measurement.
In a second aspect, an embodiment of the present invention further provides an interconnected microgrid electric energy transaction and system operation cooperative decision making system, where the microgrid is interconnected through a flexible switch device, and 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 driving distribution robust optimization model of a single microgrid by taking the minimum running cost of each microgrid as an objective function based on a given P2P trading electricity price, and the data driving distribution robust optimization model of the single microgrid comprises a power flow 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 establishing module is used for driving a distributed robust optimization model according to the data of the single microgrid and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as a target function;
the conversion module is used for performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision 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 stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the interconnected microgrid P2P electric energy transaction and system operation cooperative decision method provided by the embodiment of the invention.
In the embodiment of the invention, based on a given P2P transaction electricity price, a data-driven distribution robust optimization model of a single microgrid is established by taking the minimum operation cost of each microgrid as an objective function, wherein the data-driven distribution robust optimization model of the single microgrid comprises a power flow model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy 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; driving a distributed robust optimization model according to the data of the single microgrid, and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as a target function; and performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model. The three-phase asymmetric operation of the micro-grid and the operation constraint of the power grid can be processed, a distribution robust optimization model of the independent micro-grid is established, and the operation cost is optimized under the condition of giving P2P trade electricity price; a novel decentralized electricity price mechanism is designed for P2P electric energy trading to determine P2P trading electricity price reflecting market value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an interconnected microgrid electric energy transaction and system operation cooperative decision method provided by an embodiment of the present invention;
fig. 1a is a schematic connection diagram of two interconnected three-phase asymmetric micro-grids according to an embodiment of the present invention;
fig. 1b is a schematic diagram of a framework of a collaborative optimization method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of energy procurement of a microgrid 1 according to an embodiment of the present invention;
fig. 3 is a schematic diagram of microgrid 2 energy procurement provided by an embodiment of the present invention;
fig. 4 is a schematic diagram of microgrid 3 energy procurement provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of energy procurement of a microgrid 4 according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an hourly transaction price provided by an embodiment of the present invention;
fig. 7 is a schematic diagram of social costs of different methods provided by embodiments of the present invention in different sample sizes.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the invention, a decentralized pricing scheme is provided, and the P2P trading electric energy and electricity price among multiple micro grids can be determined by using an alternating direction multiplier Algorithm (ADMM), so that the privacy and the autonomy of different micro grid bodies are protected. The invention considers the working condition of three-phase asymmetric operation of the microgrid and is tightly combined with the practical engineering problem. Meanwhile, flexible interconnection of a plurality of micro grids is realized by utilizing flexible switch equipment (SOP), so that complete controllability of power transmission among the micro grids is realized; when a certain microgrid breaks down, due to direct current connection in the SOP, quick fault isolation can be achieved. In addition, the cooperative optimization decision of P2P electric energy trading and system operation is also considered, the uncertainty of load and new energy is processed by adopting a distributed robust optimization method, and the defects that the requirement of random optimization on variable accurate probability distribution is too high and the robust optimization result is too conservative are overcome.
Referring to fig. 1, fig. 1 is a flowchart of a cooperative decision method for interconnected microgrid electric energy transaction and system operation according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
101. and establishing a data-driven distribution robust optimization model of the single microgrid by taking the minimum operation cost of each microgrid as an objective function based on the given P2P trading electricity price.
In the embodiment of the invention, the data-driven distributed robust optimization model of the single microgrid comprises a power flow 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, please refer to fig. 1a and fig. 1b, where fig. 1a is a schematic connection diagram of two interconnected three-phase asymmetric microgrids 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 of two three-phase asymmetrically operated micro grids interconnected by an SOP consisting of two back-to-back voltage source inverters. The flow transmitted through the SOP is completely controllable, which lays a technical foundation for the P2P electric energy transaction between the interconnected micro-grids. In order to promote P2P electric energy transaction and ensure operation safety and reliability, an independent micro-grid electric energy transaction and grid operation collaborative optimization model based on a data-driven distributed robust optimization method (DRO) is provided, and the general description of the proposed scheme is shown in FIG. 1 b. Each microgrid may purchase electrical energy from the main grid at a given price or be obtained from other microgrids at a contracted price in the form of a P2P transaction. Furthermore, as new energy generation and load demand are uncertain at a day-ahead stage, the DRO method is employed to deal with the uncertainty. Each microgrid schedules its own DG and BESS to minimize operating costs while reserving reserve power at a day-ahead stage for real-time power regulation using designed affine rules at a real-time stage.
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 microgrid, a reactive power model of the single microgrid and node voltage constraint in the microgrid.
In the embodiment of the present invention, a linearized three-phase line power flow (DistFlow) model is used to describe a power flow in a three-phase asymmetric microgrid, which may be specifically described as follows:
Figure BDA0002842869400000061
Figure BDA0002842869400000062
Figure BDA0002842869400000063
Figure BDA0002842869400000064
Figure BDA0002842869400000065
wherein, in the above formulas (1a) to (1e),
Figure BDA0002842869400000066
is the active power output of the distributed controllable generator g,
Figure BDA0002842869400000067
is the reactive power output of the distributed controllable generator g;
Figure BDA0002842869400000068
is the active power on line i in the microgrid m,
Figure BDA0002842869400000069
is reactive power on line i in microgrid mRate;
Figure BDA00028428694000000610
the active power flow transmitted from the microgrid n to the microgrid m is shown;
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
the average square value of the voltage of the node i in the microgrid m is obtained;
Figure BDA00028428694000000614
is the active load of the node i,
Figure BDA00028428694000000615
is the reactive load of node i;
Figure BDA00028428694000000616
binary parameters (such as 0 represents no installation, and 1 represents installation) representing whether the distributed controllable generator g is installed on the node i, binary parameters (such as 0 represents no installation, and 1 represents installation) representing whether the new energy generator k is installed on the node i, and binary parameters (such as 0 represents no installation, and 1 represents installation) representing whether the new energy generator k is installed on the node i,
Figure BDA00028428694000000617
A binary parameter indicating whether the node i is provided with the battery energy storage system b or not (for example, 0 represents no installation, and 1 represents installation);
Figure BDA00028428694000000618
whether a terminal of the SOP connected with the micro-grids m and n is connected with a bus i or not is represented; a ismaxIs the maximum tap ratio of the voltage regulator, aminIs the minimum tap ratio of the voltage regulator. Wherein the equations (1a) and (1b) represent the active and reactive power at each nodeBalancing; equation (1c) indicates that the square-mean value of the secondary side node voltage of the voltage regulator should not exceed its adjustable range, wherein [ < denotes the multiplication of the corresponding elements ]; the voltage drop over the distribution line is described by the equation (1d), where
Figure BDA00028428694000000619
Is the equivalent line impedance, is a 3x3 complex matrix; equation (1e) represents the upper and lower limits of the node voltage.
According to the power flow model of the three-phase asymmetric power grid,
Figure BDA00028428694000000620
and the active power exchange of the microgrid m and the main network at the moment t is represented. Thus, the cost (profit) of the microgrid m to purchase (sell) energy from the main grid at time t may be expressed as:
Figure BDA00028428694000000621
in the formula (2)
Figure BDA0002842869400000071
Representing the price of purchasing (selling) energy from the main network, usually
Figure BDA0002842869400000072
Above
Figure BDA0002842869400000073
(.) + denotes projection operation (x) to the non-negative quadrant+＝max(x,0)。
Likewise, the cost (profit) of the piconet m conducting P2P power trading with other piconets at time t may be expressed as:
Figure BDA0002842869400000074
in the formula (3)
Figure BDA0002842869400000075
Representing the P2P electric energy trade price between the piconets m and n.
In an embodiment of the present invention, the operation constraints of the distributed controllable generator and the new energy power generation include: the method comprises the following steps of standby power constraint of the distributed controllable generator, active output constraint of the distributed controllable generator, reactive output constraint of the distributed controllable generator, three-phase unbalance constraint and reactive output constraint of the new energy generator. Specifically, the above-mentioned distributed controllable generator (DG) operation constraint is expressed as follows:
Figure BDA0002842869400000076
Figure BDA0002842869400000077
Figure BDA0002842869400000078
Figure BDA0002842869400000079
among the equations (4a) to (4d),
Figure BDA00028428694000000710
representing the upward reserve power in the distributed controllable generator g in phi phase,
Figure BDA00028428694000000711
representing the downward standby power in the phi phase distributed controllable generator g;
Figure BDA00028428694000000712
represents the upper limit of the reactive power output of the distributed controllable generator g in phi phase,
Figure BDA00028428694000000713
representing the lower limit of reactive power output of the distributed controllable generator g in phi phase.
Wherein equation (4a) indicates that the active output of each phase of the DG is within a range in which the reserve is considered; equation (4b) indicates that the reserve value is non-negative; equation (4c) indicates that the reactive power output of each phase of the DG is within the allowable range; equation (4d) shows that the three-phase unbalance of DG does not exceed the maximum allowable value deltag。
Optionally, in order to fully utilize new energy to generate power, the active output of the RG is controlled at the maximum power point, and the reactive output of the RG is adjusted within the allowable range, as shown in the following formula:
Figure BDA0002842869400000081
in the formula (5)
Figure BDA0002842869400000082
Represents the upper limit of the reactive power of the new energy generator g,
Figure BDA0002842869400000083
and the lower limit of the reactive power of the new energy generator g.
In an embodiment of the present invention, the operation constraints of the battery energy storage system include: the method comprises the following steps of discharge power constraint of a battery energy storage system, charge 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 charge state constraint of the battery energy storage system. In particular, each battery energy storage system BESS
Figure BDA0002842869400000084
The operational constraints of (c) are as follows:
Figure BDA0002842869400000085
Figure BDA0002842869400000086
Figure BDA0002842869400000087
Figure BDA0002842869400000088
Figure BDA0002842869400000089
Figure BDA00028428694000000810
Eb,T＝Eb,0 (6g)
in the expressions (6a) to (6g), the expressions (6a) and (6b) limit the range of the charge/discharge power of the BESS for each phase, and take into account the charge/discharge reserve power of the stored energy
Figure BDA00028428694000000811
And
Figure BDA00028428694000000812
respectively representing maximum charging power and maximum discharging power; equations (6c) and (6d) ensure that the reserve power is not negative and that the maximum allowed power cannot be exceeded; equation (6e) represents the change law of the state of charge (SOC) of the BESS; equation (6f) represents the allowable range of SOC; equation (6g) indicates that the SOC at the end of the day is equal to the SOC at the beginning.
Optionally, to avoid excessive use of BESS, the battery life penalty cost is added to the objective function, which is expressed as a linear function related to charge and discharge power:
Figure BDA0002842869400000091
in the formula (7) < theta >bIs a cost factor associated with the loss of life of the BESS.
In an embodiment of the present invention, the flexible switch device operation constraint includes: power balancing of each phase of the flexible switching device and switching power constraints of the flexible switching device. Alternatively, in order to save the capacity of the SOP for active power transfer and reduce power losses, no consideration is given to generating or absorbing reactive power with the SOP. Specifically, the operation constraint of the flexible switchgear may be represented by the following equation:
Figure BDA0002842869400000092
Figure BDA0002842869400000093
Figure BDA0002842869400000094
among the equations (8a) to (8c), the equation (8a) 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 microgrid; equation (8b) represents that the power loss in the SOP is a linear function with respect to the exchange power, where
Figure BDA0002842869400000096
Is a very small loss factor (e.g., 0.02); equation (8c) indicates that the exchange power does not exceed the SOP capacity
Figure BDA0002842869400000097
In an embodiment of the present invention, the above real-time power adjustment strategy for the distributed controllable generator and the battery energy storage system with uncertain power deviation includes: affine rules of power adjustment of the distributed controllable generator, affine rules of power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
Further, when the fluctuation of the new energy and load is clearly confirmed, the reserve capacity of DG and BESS will be used to keep the power balance of each microgrid. The adjustment strategy in the embodiment of the present invention may be shown by the following formula:
Figure BDA0002842869400000098
Figure BDA0002842869400000099
Figure BDA00028428694000000910
Figure BDA0002842869400000101
Figure BDA0002842869400000102
Figure BDA0002842869400000103
Figure BDA0002842869400000104
among the formulas (9a) to (9g),
Figure BDA0002842869400000105
representing distributed controllable generatorsg is the affine factor of the phase phi,
Figure BDA0002842869400000106
an affine factor representing the phase phi of the energy storage system b. Equations (9a) and (9b) represent affine rules for DG and BESS power adjustment, where
Figure BDA0002842869400000107
Is a random variable and represents the power deviation of phi phase in the microgrid m; equations (9c) to (9e) are constraints which the affine factors need to satisfy; equations (9f) and (9g) indicate that the power adjustments of DG and BESS do not exceed the power reserve range.
It should be noted that equations (9f) and (9g) can be processed by robust optimization, but this results in over-conservative results. In fact, insufficient up power reserve and insufficient down power reserve will result in load shedding and reduced new energy output, respectively. Further, in the embodiment of the present invention, this problem can be solved by penalizing the excess amount. Further, the power reserve excess for the phi phase is a penalty function at time t, which can be expressed as:
Figure BDA0002842869400000108
in the formula (10), the above
Figure BDA0002842869400000109
Is the cost per unit power of the load shedding,
Figure BDA00028428694000001010
is the unit power cost for reducing new energy resources.
As can be seen from the affine adjustment strategy, the generation cost of DG is related to the uncertainty of load and new energy output. In order to reduce the computational complexity, the piecewise linear function is adopted to represent the power generation cost, and the total power generation cost is expressed by random variable
Figure BDA00028428694000001011
The piecewise linear function of (a) is shown by the following equation:
Figure BDA00028428694000001012
in the formula (11), the first and second groups,
Figure BDA00028428694000001013
Figure BDA00028428694000001014
and
Figure BDA00028428694000001015
is the cost coefficient of the kappa fragment;
Figure BDA00028428694000001016
Figure BDA00028428694000001017
and
Figure BDA00028428694000001018
optionally, to quantify the cost of the BESS deviation from the expected charge-discharge plan, a BESS scheduling offset cost is constructed, which is a linear function of the power deviation, as shown in the following equation:
Figure BDA0002842869400000111
in the formula (12), the reaction mixture is,
Figure BDA0002842869400000112
δb,tis the cost per power deviation of BESS b at time t;
Figure BDA0002842869400000113
and
Figure BDA0002842869400000114
in the embodiment of the present invention, can be used
Figure BDA0002842869400000115
The total cost associated with uncertainty at time t, i.e., the sum of DG generation cost, BESS scheduling offset cost, and penalty cost for exceeding the power reserve range, may be expressed as follows:
Figure BDA0002842869400000116
in the formula (13), the reaction mixture is,
Figure BDA0002842869400000117
is the set of decision variables of the microgrid m at time t.
In the embodiment of the present invention, the goal of each microgrid is to minimize its total operating cost, that is, a data-driven distributed robust optimization model of a single microgrid is established by taking the minimum operating cost of each microgrid as an objective function, wherein the operating cost is composed of two parts, one part is the cost related to uncertainty, and the other part is the cost unrelated to uncertainty. Where the cost 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 microgrid may be as follows:
Figure BDA0002842869400000118
Figure BDA0002842869400000119
Figure BDA00028428694000001110
in the formula (14a) and the formula (14b), the formula (14a) is an objective function, and the formula (14b) is a constraint condition. The above (14b) indicates that the constraint conditions are expression (1), expression (4) to expression (6), expression (8c), expression (9a) to expression (9 e). Here, the expression (1) may be understood as a generic term from the expression (1a) to the expression (1e), the expression (4) may be understood as a generic term from the expression (4a) to the expression (4d), and the expression (6) may be understood as a generic term from the expression (6a) to the expression (6 g). Among the equations (14a) to (14b),
Figure BDA00028428694000001111
102. and driving a distributed robust optimization model according to the data of a single microgrid, and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as an objective function.
In an embodiment of the present invention, the goal of the multi-microgrid energy management model is to minimize the total social cost. The total social cost may be obtained by adding the operating cost of each microgrid. Optionally, since the P2P electric energy transaction belongs to the internal behavior among multiple micro-networks, the cost and the profit of the part are mutually offset in the objective function. The multi-microgrid energy management model can be represented by the following formula:
Figure BDA0002842869400000121
Figure BDA0002842869400000122
Figure BDA0002842869400000123
in formulae (15a) to (15c), in
Figure BDA0002842869400000124
Since the power loss of the SOP is much smaller than the power of SOP transmission, the power loss in the SOP can be ignored, thereby reducing equation (8a) to equation (15 c).
In the embodiment of the invention, a pricing algorithm is deduced, and auxiliary variables are introduced
Figure BDA0002842869400000125
And converting (15c) equivalently to the form:
Figure BDA0002842869400000126
Figure BDA0002842869400000127
103. and performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model.
In the embodiment of the invention, decentralized pricing calculation can be carried out on the multi-microgrid energy management model by an alternative direction multiplier method; converting the multi-microgrid energy management model into a linear programming model through fuzzy set-based Wasserstein measurement.
In an embodiment of the present invention, the lagrange multiplier (dual variable) associated with the constraint (15c) represents the shadow price of the P2P power transaction. Thus, ADMM-based decentralization pricing algorithms can be employed
Figure BDA0002842869400000128
Representing sets of auxiliary variables, i.e.
Figure BDA0002842869400000129
Figure BDA00028428694000001210
Is the lagrangian multiplier for the corresponding constraint (16b) forming an augmented lagrangian function as follows:
Figure BDA0002842869400000131
in the formula (17):
Figure BDA0002842869400000132
Figure BDA0002842869400000133
and
Figure BDA0002842869400000134
in the embodiment of the present invention, τ may be used to represent the number of iterations, and the iteration process of the decentralized pricing algorithm is as follows:
first, x is updatedm: since the augmented Lagrangian function and the constraint (15b) have a decomposable form structure, each independent microgrid can autonomously update x related to itselfmAnd (4) variable quantity. Fixing the auxiliary variables obtained from the last iteration
Figure BDA0002842869400000135
And lagrange multiplier
Figure BDA0002842869400000136
Then, a Lagrange function is optimized, and the micro-grid m can realize the pair variable
Figure BDA0002842869400000137
The specific update may be as shown in the following example:
Figure BDA0002842869400000138
after solving the equation (18), the microgrid m will be ready to be used
Figure BDA0002842869400000139
The value of (d) is transmitted to the piconet n.
Then y is updatedm: the auxiliary variable y is updated by solving the following equation:
Figure BDA00028428694000001310
s.t.(16a) (19b)
the equation (19a) is a solution target, the equation (19b) is a constraint condition, and the constraint condition is an equation (16 a).
By mathematical operations, the following analytical solution can be derived:
Figure BDA00028428694000001311
as can be seen from equation (20), each independent piconet can autonomously update its own related variable ym. By reception from piconets n
Figure BDA00028428694000001312
And
Figure BDA00028428694000001313
microgrid m-based equation (20) update
Figure BDA00028428694000001314
Finally, update the lambdam: the update may be made according to the following equation:
Figure BDA00028428694000001315
in equation (21), the microgrid m locally updates the lagrangian multiplier and will then
Figure BDA0002842869400000141
The value of (d) is transmitted to the piconet n.
The following equations (20) and (21) show
Figure BDA0002842869400000142
When the consistency constraint (16b) is satisfied, equation (18) is equivalent to equation (14), which means that the converged solution reaches nash equilibrium.
Optionally, a DROs (multi-microgrid energy management model) equation (15) may be converted into a linear programming problem by using Wasserstein measurement, so that convergence of the ADMM algorithm is ensured.
It should be noted that, in practical applications, the probability distribution of the random variable is usually unknown, and only one set of historical samples is available
Figure BDA0002842869400000143
May be used. According to the embodiment of the invention, the probability distribution fuzzy set can be constructed through Wasserstein Measurement (WM)
Figure BDA0002842869400000144
Thus, when data outside the sample set is used, the simulation effect does not deviate too much; ensuring convergence, namely when the number of samples tends to infinity, the fuzzy set converges to a true probability distribution; the problem which is originally unsolvable can be equivalently converted into a solvable optimization problem, and calculation is convenient.
In particular, given a set of historical samples, the probability distribution is based
Figure BDA0002842869400000145
An estimated P value can be obtained, wherein
Figure BDA0002842869400000146
To represent
Figure BDA0002842869400000147
Dirac measure of. WM is a method that describes the "distance" between the estimated distribution and the true distribution, defined as:
Figure BDA0002842869400000148
in the formula (22), xi is a tight set of random variables, Π is
Figure BDA0002842869400000149
And
Figure BDA00028428694000001410
edge distribution w and
Figure BDA00028428694000001411
the joint distribution of (1). Further, the fuzzy set structure is shown by the following equation:
Figure BDA00028428694000001412
in equation (13), ε (N) is the fuzzy set
Figure BDA00028428694000001413
Is centered on
Figure BDA00028428694000001414
It is a function of the confidence level β and the number of samples N, and can be expressed in particular by the following equation:
Figure BDA00028428694000001415
in the equation (24), D is a constant representing the random variable support diameter.
Further, to reduce the computational complexity, the expected cost value in the worst case of the probability distribution is approximated by an upper bound function, as follows:
Figure BDA0002842869400000151
in equation (25), the first two parts on the right represent the worst-case expected power generation cost and BESS scheduling offset cost values, respectively, and the last part is the worst-case expected penalty cost value that exceeds the power reserve range. From equations (11) and (12), the expected values for the worst-case generation cost and the BESS scheduling offset cost can be summarized as:
Figure BDA0002842869400000152
suppose that the support set of w can be represented as a polyhedron, for example: xi ═ w, { w: Cw ≦ d }; the formula (26) can be converted into:
Figure BDA0002842869400000153
Figure BDA0002842869400000154
Figure BDA0002842869400000155
γjκ≥0 (27d)
the above-mentioned equations (27a) to (27d) may be collectively referred to as an equation (27), and the above-mentioned equation (27) is for x and { mu, sj,γjkThe linear programming problem.
Figure BDA0002842869400000156
Can be equivalently rewritten as:
Figure BDA0002842869400000157
Figure BDA0002842869400000158
due to the fact that
Figure BDA0002842869400000159
Is about wφPoint-by-point supremum of the linear function set of (1), which is with respect to wφA convex function of (a). To the left of equation (28b) is to maximize a convex function within a compact interval; maximum points are contained inW φ，
Figure BDA0002842869400000161
And
Figure BDA0002842869400000162
among the three. Thus, equation (28) can be further converted into:
Figure BDA0002842869400000163
Figure BDA0002842869400000164
Figure BDA0002842869400000165
Figure BDA0002842869400000166
the formula (29) is about
Figure BDA0002842869400000167
The linear programming problem of (2).
Therefore, the equations (15) and (18) can be converted into a linear programming model. And performing interconnected microgrid electric energy transaction and system operation cooperative decision through a linear programming model.
In the embodiment of the invention, based on a given P2P transaction electricity price, a data-driven distribution robust optimization model of a single microgrid is established by taking the minimum operation cost of each microgrid as an objective function, wherein the data-driven distribution robust optimization model of the single microgrid comprises a power flow model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy 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; driving a distributed robust optimization model according to the data of the single microgrid, and establishing a multi-microgrid energy management model by taking the minimum total operation cost of all the microgrids as a target function; and performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model. The three-phase asymmetric operation of the micro-grid and the operation constraint of the power grid can be processed, a distribution robust optimization model of the independent micro-grid is established, and the operation cost is optimized under the condition of giving P2P trade electricity price; a novel decentralized electricity price mechanism is designed for P2P electric energy trading to determine P2P trading electricity price reflecting market value.
In order to further explain the effect of the embodiment of the invention, the embodiment of the invention tests a multi-microgrid system constructed by IEEE 123 node data, and the system consists of 4 three-phase asymmetric operation microgrids. The effectiveness of the invention is verified through simulation analysis.
Specifically, table 1 shows a comparison between the cost or the profit of the microgrid when the multi-microgrid system applies P2P electric energy trading and the microgrid system does not apply P2P electric energy trading.
TABLE 1 cost and benefit of microgrid power transactions
Figure BDA0002842869400000171
As shown in table 1, by applying the cooperative decision method for the electric energy transaction of the interconnected microgrid and the system operation, the operation cost of the microgrid is reduced, the total electric energy purchase cost is reduced by 20%, and the effectiveness of the P2P electric energy transaction scheme and the decentralized pricing method provided by the patent is verified.
Referring to fig. 2 to 5, fig. 2 is a schematic diagram of energy procurement of a ss 1 according to an embodiment of the present invention, fig. 3 is a schematic diagram of energy procurement of a ss 2 according to an embodiment of the present invention, fig. 4 is a schematic diagram of energy procurement of a ss 3 according to an embodiment of the present invention, and fig. 5 is a schematic diagram of energy procurement of a ss 4 according to an embodiment of the present invention, as shown in fig. 2 to 5, surplus power of the ss 1 is sold to the ss 4, most of the surplus power of the ss 3 is sold to the ss 2, and the rest is sold to the ss 4. At 10 to 15 points, the photovoltaic output is relatively high, the electric energy generated by the microgrid can meet the requirements of all loads and buyers, and as can be seen from the figure, the microgrid is more prone to trade with other microgrids 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, λbIndicating the price of electricity, lambda, for purchasing power from the main networksIndicating the price of electricity, lambda, for selling electric energy to the main gridijAnd P2P transaction electricity prices of the microgrid i and the microgrid j are represented. As can be seen from FIG. 6, the P2P trade price of electricity is at λsAnd λbIn between, both trading parties can benefit by participating in the P2P trade, and the electricity price mechanism provided by the patent is proved to have an incentive 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, under different sample sizes, the optimal social cost is obtained by Robust Optimization (RO), Distributed Robust Optimization (DRO), and random optimization (SP), respectively. As can be seen from the figure, the RO method is too conservative and the resulting cost is the highest. The SP method underestimates the actual cost and the resulting cost is the lowest. The cost derived by the DRO method is between RO and SP, representing the worst case cost in a given fuzzy set. With increasing samples, the cost of the DRO method is gradually reduced.
Table 2 lists the in-sample and out-of-sample costs for different sized sample sets, solved using random optimization (SP) and Distributed Robust Optimization (DRO), respectively, and compared.
TABLE 2 comparison of SP and DRO for different size sample sets
Figure BDA0002842869400000181
As can be seen from table 2, for the sample sizes examined, the expected cost in the sample using the SP method is approximated by the mean of the expected cost out of the sample, which is small and has a large deviation from reality. As the sample size increases, the deviation under the SP method is gradually reduced. In contrast, the in-sample cost using the DRO method is an upper limit of the out-of-sample cost, and both the in-sample cost and the out-of-sample mean using the DRO method are in a downward trend as the sample size increases.
Table 3 lists the in-sample cost and the out-of-sample mean obtained using the DRO, deterministic algorithm (DET) and RO methods.
TABLE 3 DRO, DET vs. RO Algorithm
Figure BDA0002842869400000182
As can be seen from table 3, the intra-sample cost is highest using the RO method, while the out-of-sample mean value is highest using the DET method since the DET method does not take uncertainty into account. In contrast, using the DRO method, which describes uncertainty, yields the lowest intra-sample cost and extra-sample mean, can reduce cost. Thus, DRO overcomes the problem of RO being too conservative, while being more robust than SP.
It should be noted that the interconnected microgrid electric energy transaction and system operation cooperative decision method 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 interconnected microgrid electric energy transaction and system operation cooperative decision.
Optionally, the interconnected microgrid electric energy transaction and system operation cooperative decision making system provided in the embodiment of the present 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 driving distribution robust optimization model of a single microgrid by taking the minimum running cost of each microgrid as an objective function based on a given P2P trading electricity price, and the data driving distribution robust optimization model of the single microgrid comprises a power flow 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 establishing module is used for driving a distributed robust optimization model according to the data of the single microgrid and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as a target function;
the conversion module is used for performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model.
Optionally, the operating cost includes uncertainty-related operating cost and uncertainty-independent operating cost.
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 microgrid, a reactive power model of the single microgrid and node voltage constraint in the microgrid.
Optionally, the operation constraints of the distributed controllable generator and the new energy power generation include: the method comprises the following steps of standby power constraint of the distributed controllable generator, active output constraint of the distributed controllable generator, reactive output constraint of the distributed controllable generator, three-phase unbalance constraint and reactive output constraint of the new energy generator.
Optionally, the operation constraint of the battery energy storage system includes: the method comprises the following steps of discharge power constraint of a battery energy storage system, charge 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 charge state constraint of the battery energy storage system.
Optionally, the flexible switchgear operation constraints include: power balancing of each phase of the flexible switching device and switching power constraints of the flexible switching device.
Optionally, the strategy for adjusting the real-time power of the distributed controllable generator and the battery energy storage system according to the uncertain power deviation includes: affine rules of power adjustment of the distributed controllable generator, affine rules of 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 alternative 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 a fuzzy set.
It should be noted that the interconnected microgrid electric energy transaction and system operation cooperative decision making system provided by the embodiment of the present invention may be applied to a mobile phone, a monitor, a computer, a server, and other devices that can perform an interconnected microgrid electric energy transaction and system operation cooperative decision.
The interconnected microgrid electric energy transaction and system operation cooperative decision making system provided by the embodiment of the invention can realize each process realized by the interconnected microgrid electric energy transaction and system operation cooperative decision making system method in the method embodiment, and can achieve the same beneficial effect. To avoid repetition, further description is omitted here.
Optionally, an electronic device provided in 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 trading electricity price, establishing a data-driven distribution robust optimization model of a single microgrid by taking the minimum operation cost of each microgrid as an objective function, wherein the data-driven distribution robust optimization model of the single microgrid comprises a power flow model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy 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;
driving a distributed robust optimization model according to the data of the single microgrid, and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as a target function;
and performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model.
Optionally, the operating cost includes uncertainty-related operating cost and uncertainty-independent operating cost.
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 microgrid, a reactive power model of the single microgrid and node voltage constraint in the microgrid.
Optionally, the operation constraints of the distributed controllable generator and the new energy power generation include: the method comprises the following steps of standby power constraint of the distributed controllable generator, active output constraint of the distributed controllable generator, reactive output constraint of the distributed controllable generator, three-phase unbalance constraint and reactive output constraint of the new energy generator.
Optionally, the operation constraint of the battery energy storage system includes: the method comprises the following steps of discharge power constraint of a battery energy storage system, charge 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 charge state constraint of the battery energy storage system.
Optionally, the flexible switchgear operation constraints include: power balancing of each phase of the flexible switching device and switching power constraints of the flexible switching device.
Optionally, the strategy for adjusting the real-time power of the distributed controllable generator and the battery energy storage system according to the uncertain power deviation includes: affine rules of power adjustment of the distributed controllable generator, affine rules of power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
Optionally, the step of performing decentralized pricing calculation on the multi-microgrid energy management model by the processor, and converting the multi-microgrid energy management model into a linear programming model specifically includes:
performing decentralized pricing calculation on the multi-microgrid energy management model through an alternative direction multiplier method;
converting the multi-microgrid energy management model into a linear programming model through fuzzy set-based Wasserstein measurement.
It should be noted that the electronic device may be a device that can be applied to a mobile phone, a monitor, a computer, a server, and the like that can perform an interconnected microgrid electric energy transaction and system operation cooperative decision.
The electronic device provided by the embodiment of the invention can realize each process realized by the interconnected microgrid electric energy transaction and system operation cooperative decision method in the method embodiment, can achieve the same beneficial effect, and is not repeated here to avoid repetition.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program realizes each process of the interconnected microgrid electric energy transaction and system operation cooperative decision method provided by the embodiment of the invention, can achieve the same technical effect, and is not repeated here to avoid repetition.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes 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 (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (10)

1. A cooperative decision-making method for electric energy transaction and system operation of interconnected micro-grids is characterized in that the micro-grids are interconnected through flexible switch equipment, and the cooperative decision-making method comprises the following steps:
based on a given P2P trading electricity price, establishing a data-driven distribution robust optimization model of a single microgrid by taking the minimum operation cost of each microgrid as an objective function, wherein the data-driven distribution robust optimization model of the single microgrid comprises a power flow model of a three-phase asymmetric power grid, operation constraints of a distributed controllable generator and new energy 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;
driving a distributed robust optimization model according to the data of the single microgrid, and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as a target function;
and performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model.
2. The interconnected microgrid electrical energy transaction and system operation cooperative decision method of claim 1, wherein the operation costs include uncertainty-related operation costs and uncertainty-independent operation costs.
3. The interconnected microgrid electric energy transaction and system operation cooperative decision method of 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 microgrid, a reactive power model of the single microgrid and node voltage constraint in the microgrid.
4. The interconnected microgrid electric energy transaction and system operation cooperative decision method of claim 1, wherein the operation constraints of the distributed controllable generators and the new energy generation comprise: the method comprises the following steps of standby power constraint of the distributed controllable generator, active output constraint of the distributed controllable generator, reactive output constraint of the distributed controllable generator, three-phase unbalance constraint and reactive output constraint of the new energy generator.
5. The interconnected microgrid electric energy transaction and system operation cooperative decision method of claim 1, wherein the operation constraints of the battery energy storage system include: the method comprises the following steps of discharge power constraint of a battery energy storage system, charge 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 charge state constraint of the battery energy storage system.
6. The interconnected microgrid electrical energy transaction and system operation cooperative decision method of claim 1, wherein the flexible switchgear operation constraints comprise: power balancing of each phase of the flexible switching device and switching power constraints of the flexible switching device.
7. The interconnected microgrid electric energy transaction and system operation cooperative decision method of claim 1, wherein the real-time power adjustment strategy for the distributed controllable generators and the battery energy storage systems with uncertain power deviation comprises: affine rules of power adjustment of the distributed controllable generator, affine rules of power adjustment of the battery energy storage system, affine factor constraint conditions and excess penalty conditions.
8. The interconnected microgrid electric energy transaction and system operation cooperative decision method of claim 1, wherein the step of performing decentralized pricing calculation on the multi-microgrid energy management model and converting the multi-microgrid energy management model into a linear programming model specifically comprises:
performing decentralized pricing calculation on the multi-microgrid energy management model through an alternative direction multiplier method;
converting the multi-microgrid energy management model into a linear programming model through fuzzy set-based Wasserstein measurement.
9. The utility model provides an interconnection microgrid electric energy transaction and system operation decision-making system in coordination, its characterized in that, interconnect through flexible switchgear between the microgrid, 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 driving distribution robust optimization model of a single microgrid by taking the minimum running cost of each microgrid as an objective function based on a given P2P trading electricity price, and the data driving distribution robust optimization model of the single microgrid comprises a power flow 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 establishing module is used for driving a distributed robust optimization model according to the data of the single microgrid and establishing a plurality of flexibly interconnected multi-microgrid energy management models by taking the minimum total operating cost of all the microgrids as a target function;
the conversion module is used for performing decentralized pricing calculation on the multi-microgrid energy management model, converting the multi-microgrid energy management model into a linear programming model, and performing interconnected microgrid electric energy transaction and system operation cooperative decision through the linear programming model.
10. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the interconnected microgrid electrical energy transaction and system operation cooperative decision method according to any one of claims 1 to 8.
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