CN115764863A - Multi-microgrid cooperative operation method based on data driving - Google Patents

Multi-microgrid cooperative operation method based on data driving Download PDF

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CN115764863A
CN115764863A CN202211359585.7A CN202211359585A CN115764863A CN 115764863 A CN115764863 A CN 115764863A CN 202211359585 A CN202211359585 A CN 202211359585A CN 115764863 A CN115764863 A CN 115764863A
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microgrid
power
electricity price
energy
model
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张敏
赵军
常潇
王腾鑫
郭翔宇
宋金鸽
李慧蓬
韩肖清
杜佳男
李廷钧
尹泽华
周鑫
白桦
马佳琪
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State Grid Electric Power Research Institute Of Sepc
Taiyuan University of Technology
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State Grid Electric Power Research Institute Of Sepc
Taiyuan University of Technology
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Abstract

The invention discloses a multi-microgrid cooperative operation method based on data driving, and relates to the field of power dispatching operation of a microgrid. Firstly, establishing a microgrid model under a carbon transaction mechanism, and then establishing a cooperative operation model of a multi-microgrid under the carbon transaction mechanism by utilizing a Nash bargaining theory based on an energy interaction mechanism among the microgrids so as to discuss the influence of cooperative operation of the multi-microgrid under the carbon transaction mechanism on the environmental protection and the economical efficiency of a system; according to historical electricity price reference data, a large number of electricity price scenes meeting the requirements are generated by adopting a Monte Carlo method, and the electricity price scenes are reduced to be typical scenes for equivalence so as to analyze the influence of electricity price fluctuation; because the probability distribution of the renewable energy output can not be accurately obtained, the wind-solar output uncertainty influence is analyzed by adopting a robust optimization method and through dual conversion without knowing the accurate distribution of the wind-solar output. The method and the device can improve the benefits of the system and improve the risk of the multi-microgrid collaborative system for coping with the electricity price and wind and light uncertainty.

Description

Multi-microgrid cooperative operation method based on data driving
Technical Field
The invention relates to the field of power dispatching operation of micro-grids, in particular to a multi-micro-grid cooperative operation method based on data driving.
Background
The development of various forms of low-carbon new energy is a main measure for reducing carbon emission, and the construction of a novel power system taking the new energy as a main body becomes an important direction for power system innovation. The micro-grid is used as an important component of a novel power system, and is provided with devices for new energy power generation, energy storage and the like, so that the energy utilization rate can be improved. With the increase of micro grid access systems, adjacent Micro Grids (MGs) can form a multi-micro grid system, thereby realizing the cooperative complementation of energy. However, each microgrid has different operation requirements, and therefore, it is very important to coordinate the operation relationship among the multiple principals.
The game idea related to the problem is applied to the optimization scheduling of the current power system, and can be divided into two types, namely non-cooperative play and cooperative game. In the first category, the entities are often divided into opposing buyers and sellers based on their energy deficit characteristics, targeted at a maximum profit for each and competing with the stackelberg game method. However, the non-cooperative game emphasizes individual benefits, cannot ensure the existence and uniqueness of the Nash equilibrium solution, and often cannot realize the optimal overall benefits. In the second category, energy transactions between multiple principals are implemented according to cooperative game theory, often modeled as a league game or nash bargaining model. However, the league game model is a centralized optimization algorithm, a large amount of information of each subject needs to be utilized, privacy of the subjects is leaked, and the nash bargaining model adopting an alternating direction method (ADMM) can effectively protect the privacy of each subject and has lower computational complexity compared with the league game. Therefore, a multi-microgrid cooperative operation method is established based on Nash negotiation theory.
In the multi-microgrid cooperative operation process, because new energy, stored energy and various novel loads are involved, the multi-microgrid cooperative operation process comprises an energy link (such as an energy storage system and the like) which is easy to control and an energy link (such as wind power generation, photovoltaic power generation and the like) which is intermittent and difficult to control, so that the multi-microgrid cooperative scheduling becomes a complex multi-main-body, constrained, nonlinear and random uncertain hybrid integer type combined optimization problem, and the processing of large-scale data is lacked. In addition, a multi-agent collaborative optimization scheduling method considering a carbon transaction mechanism is needed to be established, but the influence of collaborative operation of the multi-agent under the carbon transaction mechanism on the environmental protection of the system is not discussed in the prior art. It should be noted that the intermittency of wind and light output and the volatility of electricity prices can cause adverse effects on the multi-microgrid cooperative operation, however, currently, researches on uncertainty of wind and light output mainly focus on a single microgrid, researches on uncertainty of electricity prices mainly focus on the power market level, and the influences on the multi-microgrid cooperative operation are rarely analyzed. In addition, it should be noted that although the latest technology researches the influence of the uncertainty of the electricity price and the game fraud on the multi-microgrid cooperative operation, the processing on the uncertainty of the electricity price adopts a robust optimization method, does not consider massive power market data, cannot be applied to a big data scene, and does not consider the influence of a carbon transaction mechanism, so that the research is one-sided. Therefore, a multi-microgrid cooperative operation method considering uncertainty of electricity prices based on data driving under a carbon transaction mechanism is urgently needed to be established, benefit requirements of all participants are reasonably balanced through Nash negotiation game theory, system operation economy and environmental friendliness are effectively improved, and processing capacity of large-scale data is effectively improved.
Disclosure of Invention
The invention provides a multi-microgrid cooperative operation method based on data driving, aiming at solving the problems that cooperative operation under a carbon transaction mechanism and large-scale data processing lack of consideration or discussion are not yet caused by multi-microgrid cooperative operation.
The invention is realized by the following technical scheme: a multi-microgrid cooperative operation method based on data driving is characterized in that: the method comprises the following steps:
1. constructing a multi-microgrid system framework under a carbon transaction mechanism:
each micro-grid consists of a new energy power generation, energy storage, an electric heating load and a distributed energy unit, and is provided with a micro-grid energy management system (MEMS), wherein the distributed energy unit comprises a gas turbine, a gas boiler and a ground source heat pump; each microgrid is intelligently controlled and scheduled by an MEMS; the MEMS has the functions of power generation optimization scheduling, data analysis, load management and the like. Each micro-grid can carry out interaction of electric energy and communication with the main grid through the MEMS, carbon emission right trading is carried out on the carbon trading platform, cooperative operation of multiple micro-grids is achieved, and the purposes of improving operation benefits and reducing carbon emission are achieved
2. Establishing a microgrid model considering a carbon transaction mechanism:
the carbon emission quota is reasonably formulated by combining with the domestic power generation condition, if the actual carbon emission of each power generator exceeds the distributed carbon emission quota, the lacking carbon emission quota needs to be purchased on the carbon trading platform, otherwise, the remaining quota can be sold on the carbon trading platform, and the carbon emission quota is reasonably adjusted under the environment-friendly condition; the carbon transaction mechanism model of the microgrid comprises a carbon emission right quota model of the microgrid and an actual carbon emission model:
(1) Carbon transaction mechanism model of microgrid
Carbon emission quota for microgrid
Figure BDA0003921821560000031
Calculated from the following formula:
Figure BDA0003921821560000032
wherein
Figure BDA0003921821560000033
Is the power generated by the gas turbine,
Figure BDA0003921821560000034
is the heating power of the gas-fired boiler,
Figure BDA0003921821560000035
is the generated power of the new energy unit, epsilon 0 Distributing coefficients for the carbon emission weight quota, wherein i represents the ith microgrid;
actual carbon emission of the microgrid
Figure BDA0003921821560000036
Calculated from the following formula:
Figure BDA0003921821560000037
wherein
Figure BDA0003921821560000038
Is the carbon emission coefficient;
(2) The single microgrid independent operation model is as follows:
the gas turbine electric heat output and constraint conditions are as follows:
Figure BDA0003921821560000039
Figure BDA00039218215600000310
in the formula: eta GT The power generation efficiency of the gas turbine; h LHV Is natural gas with low heat value;
Figure BDA00039218215600000311
consuming the gas quantity for the gas turbine; eta GT,h Is the gas turbine heating coefficient;
the gas boiler burns natural gas to generate heat, the ground source heat pump is used as an important component of the electric heating coupling unit and can convert electric energy into heat energy, and the heating power and the constraint conditions are as follows:
Figure BDA00039218215600000312
in the formula:
Figure BDA00039218215600000313
the heating power of the gas boiler is obtained;
Figure BDA00039218215600000314
respectively the heating power and the power consumption power of the heat pump;
the electric heating flexible load in the micro-grid comprises a reducible load and a transferable load, and the constraint conditions are as follows:
Figure BDA00039218215600000315
in the formula:
Figure BDA00039218215600000316
predicting load power for electric heating;
Figure BDA00039218215600000317
the electric heating load can reduce power;
Figure BDA00039218215600000318
Figure BDA00039218215600000319
the electric heating load can transfer power respectively; further comprising a flexible load transfer power limit constraint;
the energy storage system model comprises an electricity storage system and a heat storage system, and the relevant constraints are as follows:
Figure BDA00039218215600000320
Figure BDA0003921821560000041
μ i,t,ch +μ i,t,dis ≤1 (12)
in the formula: s i,t Is the capacity of the energy storage system, P i,t,ch 、P i,t,dis For charging and discharging power of energy storage systems, eta c 、η d For charging and discharging efficiency, mu i,t,ch 、μ i,t,dis Is a charge and discharge state zone bit;
establishing a microgrid model under a carbon transaction mechanism according to the steps (1) to (2), performing electric energy interaction with a power distribution network, and considering the uncertainty of wind, light and power output and electricity price;
(3) Modeling the uncertainty of new energy power generation:
the uncertainty of new energy power generation in each micro-grid is considered, the output of renewable energy cannot be accurately predicted, and the uncertain set U of wind and light is considered i,m Is represented as follows:
Figure BDA0003921821560000042
wherein P is i,t,m Represents the generated power of renewable energy; pre i,t,m Representing a predicted generated power of the renewable energy source;
Figure BDA0003921821560000043
representing the maximum allowable deviation, alpha, of the renewable energy source i,t,m ，β i,t,m Is a dual variable; and (3) converting and solving the uncertainty set of the wind-solar power generation according to a dual theory to obtain an electric power inequality constraint as follows:
Figure BDA0003921821560000044
s.t.α-β≥-1,α≥0,β≥0
(4) Modeling the uncertainty of the electricity price:
the power market has an important influence on the decision of a multi-microgrid system, a large number of electricity price scenes meeting the requirements are generated by adopting a Monte Carlo method through the analysis of historical electricity price data, the number of the electricity price scenes is more than or equal to 1000, and the electricity price scenes are reduced to be typical scenes for equivalence, so that the multi-microgrid cooperative operation method considering the uncertainty of the electricity price based on data driving is established: carrying out weighted summation on the reduced electricity price typical scene to obtain an equivalent electricity price selling curve so as to reduce the influence of uncertainty of the electricity price on the multi-microgrid cooperative operation; for example: through analysis of a large amount of historical electricity price data, 1000 sets of electricity price scenes meeting requirements are generated by adopting a Monte Carlo method, and the electricity price scenes are reduced into 10 sets of typical scenes.
(5) An objective function:
based on the steps (1) to (4), an objective function of a single microgrid model based on data-driven electricity price uncertainty is established under a carbon transaction mechanism and is as follows:
Figure BDA0003921821560000051
Figure BDA0003921821560000052
Figure BDA0003921821560000053
Figure BDA0003921821560000054
in the formula:
Figure BDA0003921821560000055
for the cost of each microgrid operating independently,
Figure BDA0003921821560000056
the fuel cost of the micro-grid, the operation and maintenance cost of internal equipment, the load demand response cost, the cost of interaction with the main grid and the carbon emission cost are respectively; c. C gas As a price of natural gas, c GT 、c GB 、c HP 、c RES 、c ES For the operating maintenance cost factor of the respective plant, c tran 、c cut Is the demand response cost coefficient of the load, mu buy 、μ sell Purchase price and sale price for the main network, c car Is a carbon emission cost coefficient;
3. establishing a multi-microgrid cooperative operation optimization model based on Nash negotiation theory:
assuming that each microgrid belongs to different units, and allowing energy interaction and profit distribution with adjacent microgrids; establishing a multi-microgrid cooperative operation optimization model according to the Nash bargaining theory as follows:
Figure BDA0003921821560000057
Figure BDA0003921821560000058
Figure BDA0003921821560000059
ρ ij ＞λ s (25)
in the formula: n is the number of piconets participating in Nash negotiation;
Figure BDA00039218215600000510
cost before cooperation for each microgrid;
Figure BDA00039218215600000511
for the cost, rho, of each microgrid after participating in the electric energy sharing ij The electricity price between the microgrid i and the microgrid j is represented; tau. i The more than 0 indicates that the microgrid i charges the microgrid j; the Nash negotiation cooperative game model (21) is a non-convex non-linear optimization problem in nature and is difficult to directly solve; it is transformed into the following two easily solved sub-problems: the multi-microgrid system profit maximization problem and the profit sharing sub-problem are as follows:
sub-problem 1: profit maximization sub-problem of multi-microgrid system
Figure BDA00039218215600000512
Sub-problem 2: profit sharing subproblems
Figure BDA0003921821560000061
In the formula (I), the compound is shown in the specification,
Figure BDA0003921821560000062
is the optimal solution found in sub-problem 1;
4. solving a multi-microgrid cooperative operation optimization model based on an ADMM principle:
considering that the objective functions and constraint conditions of the two subproblems are non-convex, performing distributed solution by adopting an alternative direction multiplier method;
(1) Sub-problem 1 solution based on ADMM:
introducing lagrange multiplier lambda ij And constructing an augmented Lagrangian function by using a penalty factor rho:
Figure BDA0003921821560000063
decomposing the formula (28) according to an ADMM algorithm principle to obtain a distributed optimization operation model of each microgrid; the method comprises the following steps of establishing a distributed algorithm of a benefit maximization subproblem of a multi-microgrid system, and specifically comprising the following steps:
(1) setting the maximum iteration times, wherein the convergence precision is 0.1, and the penalty factor rho =0.01;
(2) solving the distributed optimization model of each microgrid to obtain interactive electric power among the microgrids of each iteration;
(3) updating Lagrange multiplier:
Figure BDA0003921821560000064
(4) judging the convergence condition of the algorithm, if the convergence precision is met, terminating the iteration, otherwise, repeating the steps;
(2) ADMM-based sub-problem 2 solution:
obtaining optimal expected transaction amount between micro grids by solving subproblem 1
Figure BDA0003921821560000065
Substituting into the electric energy trade profit maximization subproblem and introducing a Lagrange multiplier sigma ij And constructing an augmented Lagrangian function by using a penalty factor gamma:
Figure BDA0003921821560000066
decomposing the formula (29) according to an ADMM algorithm principle to obtain a distributed optimization operation model of each microgrid; here, the distributed algorithm is the same as sub-problem 1.
Compared with the prior art, the invention has the following beneficial effects: according to the data-driven multi-microgrid cooperative operation method provided by the invention, firstly, a micro energy network model under a carbon transaction mechanism is established, carbon transaction between the microgrids is allowed, then, based on an energy interaction mechanism between the microgrids, a cooperative operation model of the multi-microgrid under the carbon transaction mechanism is established by utilizing a Nash bargaining theory, and the result shows that the technical scheme of the invention makes up the defects of the prior art, the multi-microgrid cooperative operation under the carbon transaction mechanism can improve the benefits of the system and reduce the emission of carbon dioxide; according to historical electricity price reference data, a large number of electricity price scenes meeting requirements are generated by adopting a Monte Carlo method, and the electricity price scenes are reduced to be typical scenes for equivalence so as to analyze the influence of electricity price fluctuation; because the probability distribution of the renewable energy output cannot be accurately known, the wind-solar output uncertainty influence is analyzed by adopting a robust optimization method and through dual conversion without knowing the accurate distribution of the wind-solar output. The result shows that the multi-microgrid cooperative operation method considering data driving is established, large-scale data processing is facilitated, electricity price and wind and light uncertainty are considered, and the capability of the multi-microgrid cooperative system for dealing with the risk of electricity price and wind and light uncertainty can be improved.
Drawings
FIG. 1 is a flow chart of the operation of the present invention.
Fig. 2 is a multi-piconet system framework diagram according to an embodiment of the invention.
Fig. 3 is a scene diagram of a 1000-group electricity price history.
Fig. 4 is a 10-set exemplary scene graph.
Fig. 5 is a wind and light prediction power curve diagram of a multi-microgrid system.
Fig. 6 is a graph of electrical load of the multi-microgrid system.
Fig. 7 is a graph of the thermal load of the multi-microgrid system.
Fig. 8 is a diagram illustrating an inter-microgrid electric energy transaction result.
Detailed Description
The present invention is further illustrated by the following specific examples.
A multi-microgrid cooperative operation method based on data driving, a flowchart of which is shown in fig. 1, includes the following steps:
1. a multi-microgrid system framework under a carbon transaction mechanism is constructed, as shown in fig. 2:
each micro-grid consists of a new energy power generation, energy storage, an electric heating load and a distributed energy unit, and is provided with a micro-grid energy management system MEMS, wherein the distributed energy unit comprises a gas turbine, a gas boiler and a ground source heat pump; each microgrid is intelligently controlled and scheduled by an MEMS; each micro-grid carries out interaction of electric energy and communication with the main grid through the MEMS, and carbon emission right distribution is carried out on the carbon transaction platform, so that the cooperative operation of multiple micro-grids is realized;
2. establishing a microgrid model considering a carbon transaction mechanism:
under the environment-friendly condition, the carbon emission quota is reasonably adjusted; the carbon transaction mechanism model of the microgrid comprises a carbon emission right quota model of the microgrid and an actual carbon emission model:
(1) Carbon transaction mechanism model of microgrid
Carbon emission quota for microgrid
Figure BDA0003921821560000081
Calculated from the following formula:
Figure BDA0003921821560000082
wherein
Figure BDA0003921821560000083
Is the power generated by the gas turbine,
Figure BDA0003921821560000084
is the heating power of the gas-fired boiler,
Figure BDA0003921821560000085
is the generated power of the new energy unit, epsilon 0 Distributing coefficients for carbon emission weight quota, wherein i represents the ith microgrid;
actual carbon emission of the microgrid
Figure BDA0003921821560000086
Calculated from the following formula:
Figure BDA0003921821560000087
wherein
Figure BDA0003921821560000088
Is the carbon emission coefficient;
(2) The single microgrid independent operation model is as follows:
the gas turbine electric heat output and constraint conditions are as follows:
Figure BDA0003921821560000089
Figure BDA00039218215600000810
in the formula: eta GT The power generation efficiency of the gas turbine; h LHV Is natural gas with low heat value;
Figure BDA00039218215600000811
consuming the gas quantity for the gas turbine; eta GT,h Is the gas turbine heating coefficient;
the gas boiler burns natural gas to generate heat, the ground source heat pump is used as an important component of the electric heating coupling unit and can convert electric energy into heat energy, and the heating power and the constraint conditions are as follows:
Figure BDA00039218215600000812
in the formula:
Figure BDA00039218215600000813
the heating power of the gas boiler is obtained;
Figure BDA00039218215600000814
respectively heating power and power consumption power of the heat pump;
the electric heating flexible load in the micro-grid comprises a reducible load and a transferable load, and the constraint conditions are as follows:
Figure BDA00039218215600000815
in the formula:
Figure BDA0003921821560000091
predicting load power for the electric heat;
Figure BDA0003921821560000092
the electric heating load can reduce power;
Figure BDA0003921821560000093
Figure BDA0003921821560000094
the electric heating load can transfer power respectively; further comprising a flexible load transfer power limit constraint;
the energy storage system model comprises an electricity storage system and a heat storage system, and the relevant constraints are as follows:
Figure BDA0003921821560000095
Figure BDA0003921821560000096
μ i,t,ch +μ i,t,dis ≤1 (12)
in the formula: s i,t Is the capacity of the energy storage system, P i,t,ch 、P i,t,dis For charging and discharging power of energy storage systems, eta c 、η d For charging and discharging efficiency, μ i,t,ch 、μ i,t,dis Is a charge and discharge state zone bit;
establishing a microgrid model under a carbon transaction mechanism according to the steps (1) to (2), performing electric energy interaction with a power distribution network, and considering the uncertainty of wind, light and power output and electricity price;
(3) Modeling the uncertainty of new energy power generation:
the uncertainty of new energy power generation in each micro-grid is considered, the output of renewable energy cannot be accurately predicted, and the uncertain set U of wind and light is considered i,m Is represented as follows:
Figure BDA0003921821560000097
wherein P is i,t,m Represents the generated power of renewable energy; pre i,t,m Representing a predicted generated power of the renewable energy source;
Figure BDA0003921821560000098
representing the maximum allowable deviation, alpha, of the renewable energy source i,t,m ，β i,t,m Is a dual variable; and (3) converting and solving the uncertainty set of the wind-solar power generation according to a dual theory to obtain an electric power inequality constraint as follows:
Figure BDA0003921821560000099
s.t.α-β≥-1,α≥0,β≥0
(4) Modeling the uncertainty of the electricity price:
the power market has an important influence on the decision of a multi-microgrid system, a large number of electricity price scenes meeting the requirements are generated by adopting a Monte Carlo method through the analysis of historical electricity price data, the number of the electricity price scenes is more than or equal to 1000, and the electricity price scenes are reduced to be typical scenes for equivalence, so that the multi-microgrid cooperative operation method considering the uncertainty of the electricity price based on data driving is established: carrying out weighted summation on the reduced electricity price typical scene to obtain an equivalent electricity price selling curve so as to reduce the influence of uncertainty of the electricity price on the multi-microgrid cooperative operation; through analysis of a large amount of historical electricity price data, 1000 sets of satisfactory electricity price scenes are generated by adopting a Monte Carlo method, for example, 1000 sets of electricity price scenes are shown in FIG. 3, and a typical scene of cutting the electricity price scenes shown in FIG. 3 into 10 sets is shown in FIG. 4.
(5) An objective function:
based on the steps (1) to (4), the objective function of the single microgrid model is established under a carbon transaction mechanism, and the uncertainty of the electricity price based on data driving is considered as follows:
Figure BDA0003921821560000101
Figure BDA0003921821560000102
Figure BDA0003921821560000103
Figure BDA0003921821560000104
in the formula:
Figure BDA0003921821560000105
for the cost of each microgrid operating independently,
Figure BDA0003921821560000106
fuel cost of the micro-grid, operation and maintenance cost of the internal equipment, load demand response cost, cost of interaction with the main grid, and carbon emission cost；c gas As a price of natural gas, c GT 、c GB 、c HP 、c RES 、c ES Maintenance cost factor for the operation of the respective apparatus, c tran 、c cut Is the demand response cost coefficient of the load, mu buy 、μ sell For purchase and sale of electricity for the main network, c car Is a carbon emission cost coefficient;
3. establishing a multi-microgrid cooperative operation optimization model based on Nash negotiation theory:
assuming that each microgrid belongs to different units, and allowing energy interaction and profit distribution with adjacent microgrids; establishing a multi-microgrid cooperative operation optimization model according to the Nash bargaining theory as follows:
Figure BDA0003921821560000107
Figure BDA0003921821560000108
Figure BDA0003921821560000109
ρ ij ＞λ s (25)
in the formula: n is the number of piconets participating in Nash negotiation;
Figure BDA00039218215600001010
cost before cooperation for each microgrid;
Figure BDA00039218215600001011
for the cost, rho, of each microgrid after participating in the electric energy sharing ij Representing the electricity price between the microgrid i and the microgrid j; tau. i If the sum is more than 0, the microgrid i charges a microgrid j; the Nash negotiation cooperative game model (21) is a non-convex non-linear optimization problem in nature and is difficult to directly solve; it is transformed into the following two easily solved sub-problems: multi-microgrid system profit maximization problem and profit allocation sub-problem：
Sub-problem 1: multi-microgrid system income maximization sub-problem
Figure BDA0003921821560000111
Sub-problem 2: profit sharing subproblems
Figure BDA0003921821560000112
In the formula (I), the compound is shown in the specification,
Figure BDA0003921821560000113
the optimal solution found in sub-problem 1;
4. solving a multi-microgrid cooperative operation optimization model based on an ADMM principle:
considering that the objective functions and constraint conditions of the two subproblems are non-convex, performing distributed solution by adopting an alternative direction multiplier method;
(1) Sub-problem 1 solution based on ADMM:
introducing lagrange multiplier lambda ij And constructing an augmented Lagrangian function by using a penalty factor rho:
Figure BDA0003921821560000114
decomposing the formula (28) according to an ADMM algorithm principle to obtain a distributed optimization operation model of each microgrid; the method comprises the following steps of establishing a distributed algorithm of a benefit maximization subproblem of a multi-microgrid system, and specifically comprising the following steps:
(1) setting the maximum iteration times, wherein the convergence precision is 0.1, and the penalty factor rho =0.01;
(2) solving the distributed optimization model of each microgrid to obtain interactive electric power among the microgrids of each iteration;
(3) updating Lagrange multiplier:
Figure BDA0003921821560000115
(4) judging the convergence condition of the algorithm, if the convergence precision is met, terminating the iteration, otherwise, repeating the steps;
(2) Sub-problem 2 solution based on ADMM:
obtaining optimal expected transaction amount between micro grids by solving subproblem 1
Figure BDA0003921821560000116
Substituting into the electric energy trade profit maximization subproblem and introducing a Lagrange multiplier sigma ij And constructing an augmented Lagrangian function by using a penalty factor gamma:
Figure BDA0003921821560000121
decomposing the formula (29) according to an ADMM algorithm principle to obtain a distributed optimization operation model of each microgrid; here, the distributed algorithm is the same as sub-problem 1.
5. The effectiveness of the method provided by the invention is verified by example analysis:
in this embodiment, the multi-microgrid system framework shown in fig. 1 is adopted, and a multi-microgrid operation method considering electricity price uncertainty based on data driving under a carbon transaction mechanism is described by taking three microgrids as an example, where a wind and light prediction power curve is shown in fig. 5, and an electric heating load curve is shown in fig. 6 and 7. The parameters of the micro-grid equipment such as the gas turbine are shown in the table 1.
TABLE 1 Multi-microgrid system device parameters
Figure BDA0003921821560000122
As shown in fig. 8, the multi-microgrid electric energy interactive operation result is that the microgrid 1 has a small electric load and rich wind power generation in the period of 06-07 and 18-00, and is represented as a multi-electric microgrid in the period of 12. The microgrid 2 is characterized in that photovoltaic power generation is insufficient in the period of 06. The microgrid 3 appears as a multi-electric microgrid for most of the time.
Table 2 shows the carbon emission conditions of the microgrids, and it can be seen from the table that each microgrid operates cooperatively, so that the carbon emission amount can be effectively reduced. And the carbon trading cost is a negative value, which indicates that each microgrid has sufficient power generation and large carbon quota due to renewable energy, and redundant carbon quota can be sold in the carbon trading market to obtain income. The result shows that the microgrid operates cooperatively, so that the carbon emission can be effectively reduced, the income obtained by carbon trading is improved, and the environmental protection requirement is met.
TABLE 2 analysis of carbon emissions
Figure BDA0003921821560000131
The scope of the invention is not limited to the above embodiments, and various modifications and changes may be made by those skilled in the art, and any modifications, improvements and equivalents within the spirit and principle of the invention should be included in the scope of the invention.

Claims (1)

1. A multi-microgrid cooperative operation method based on data driving is characterized in that: the method comprises the following steps:
1. constructing a multi-microgrid system framework under a carbon transaction mechanism:
each micro-grid consists of a new energy power generation, energy storage, an electric heating load and a distributed energy unit, and is provided with a micro-grid energy management system MEMS, wherein the distributed energy unit comprises a gas turbine, a gas boiler and a ground source heat pump; each microgrid is intelligently controlled and scheduled by an MEMS; each micro-grid carries out interaction of electric energy and communication with the main grid through the MEMS, and carbon emission right distribution is carried out on the carbon transaction platform, so that the cooperative operation of multiple micro-grids is realized;
2. establishing a microgrid model considering a carbon transaction mechanism:
under the environment-friendly situation, the carbon emission quota is reasonably adjusted; the carbon transaction mechanism model of the microgrid comprises a carbon emission right quota model of the microgrid and an actual carbon emission model:
(1) Carbon transaction mechanism model of microgrid
Carbon emission quota for microgrid
Figure FDA0003921821550000011
Calculated from the following formula:
Figure FDA0003921821550000012
wherein
Figure FDA0003921821550000013
Is the power generated by the gas turbine,
Figure FDA0003921821550000014
is the heating power of the gas-fired boiler,
Figure FDA0003921821550000015
is the generated power of the new energy unit, epsilon 0 Distributing coefficients for carbon emission weight quota, wherein i represents the ith microgrid;
actual carbon emissions of the microgrid
Figure FDA0003921821550000016
Calculated from the following formula:
Figure FDA0003921821550000017
wherein
Figure FDA0003921821550000018
Is the carbon emission coefficient;
(2) The single microgrid independent operation model is as follows:
the gas turbine electric heat output and constraint conditions are as follows:
Figure FDA0003921821550000019
Figure FDA00039218215500000110
in the formula: eta GT The power generation efficiency of the gas turbine; h LHV Is natural gas with low heat value;
Figure FDA00039218215500000111
consuming the gas quantity for the gas turbine; eta GT,h Is the gas turbine heating coefficient;
the gas boiler burns natural gas to generate heat, the ground source heat pump is used as an important component of the electric heating coupling unit and can convert electric energy into heat energy, and the heating power and the constraint conditions are as follows:
Figure FDA0003921821550000021
in the formula:
Figure FDA0003921821550000022
the heating power of the gas boiler is obtained;
Figure FDA0003921821550000023
respectively the heating power and the power consumption power of the heat pump;
the electric heating flexible load in the micro-grid comprises a reducible load and a transferable load, and the constraint conditions are as follows:
Figure FDA0003921821550000024
in the formula:
Figure FDA0003921821550000025
prediction of load work for electric heatingThe ratio;
Figure FDA0003921821550000026
the electric heating load can reduce power;
Figure FDA0003921821550000027
Figure FDA0003921821550000028
the electric heating load can transfer power respectively; further comprising a flexible load transfer power limit constraint;
the energy storage system model comprises an electricity storage system and a heat storage system, and the relevant constraints are as follows:
Figure FDA0003921821550000029
Figure FDA00039218215500000210
μ i,t,ch +μ i,t,dis ≤1 (12)
in the formula: s. the i,t To the capacity of the energy storage system, P i,t,ch 、P i,t,dis For charging and discharging power of the energy storage system, eta c 、η d For charging and discharging efficiency, μ i,t,ch 、μ i,t,dis Is a charge-discharge state zone bit;
establishing a microgrid model under a carbon transaction mechanism according to the steps (1) to (2), performing electric energy interaction with a power distribution network, and considering the uncertainty of wind, light and power output and electricity price;
(3) Modeling the uncertainty of new energy power generation:
the uncertainty of new energy power generation in each micro-grid is considered, the output of renewable energy cannot be accurately predicted, and the uncertain set U of wind and light is considered i,m Is represented as follows:
Figure FDA00039218215500000211
wherein P is i,t,m Represents the generated power of renewable energy; pre i,t,m Representing a predicted generated power of the renewable energy source;
Figure FDA00039218215500000212
representing the maximum allowable deviation, alpha, of the renewable energy source i,t,m ，β i,t,m Is a dual variable; and (3) converting and solving the uncertainty set of the wind-solar power generation according to a dual theory to obtain an electric power inequality constraint as follows:
Figure FDA00039218215500000213
(4) Modeling the uncertainty of the electricity price:
the power market has an important influence on the decision of a multi-microgrid system, a large number of electricity price scenes meeting the requirements are generated by adopting a Monte Carlo method through the analysis of historical electricity price data, the number of the electricity price scenes is more than or equal to 1000, and the electricity price scenes are reduced to be typical scenes for equivalence, so that the multi-microgrid cooperative operation method considering the uncertainty of the electricity price based on data driving is established: carrying out weighted summation on the reduced electricity price typical scene to obtain an equivalent electricity price selling curve so as to reduce the influence of uncertainty of the electricity price on the multi-microgrid cooperative operation;
(5) An objective function:
based on the steps (1) to (4), the objective function of the single microgrid model is established under a carbon transaction mechanism, and the uncertainty of the electricity price based on data driving is considered as follows:
Figure FDA0003921821550000031
Figure FDA0003921821550000032
Figure FDA0003921821550000033
Figure FDA0003921821550000034
in the formula:
Figure FDA0003921821550000035
for the cost of each microgrid operating independently,
Figure FDA0003921821550000036
respectively the fuel cost of the micro-grid, the operation and maintenance cost of internal equipment, the load demand response cost, the cost of interaction with the main network and the carbon emission cost; c. C gas As a price of natural gas, c GT 、c GB 、c HP 、c RES 、c ES Maintenance cost factor for the operation of the respective apparatus, c tran 、c cut Is the demand response cost coefficient of the load, mu buy 、μ sell Purchase price and sale price for the main network, c car Is a carbon emission cost coefficient;
3. establishing a multi-microgrid cooperative operation optimization model based on Nash negotiation theory:
assuming that each microgrid belongs to different units, and allowing energy interaction and profit distribution with adjacent microgrids; establishing a multi-microgrid cooperative operation optimization model according to the Nash bargaining theory as follows:
Figure FDA0003921821550000037
Figure FDA0003921821550000038
Figure FDA0003921821550000041
in the formula: n is the number of piconets participating in Nash negotiation;
Figure FDA0003921821550000042
cost before cooperation for each microgrid;
Figure FDA0003921821550000043
for the cost, rho, of each microgrid after participating in the electric energy sharing ij Representing the electricity price between the microgrid i and the microgrid j; tau is i If the sum is more than 0, the microgrid i charges a microgrid j; the Nash negotiation cooperative game model (21) is a non-convex non-linear optimization problem in nature and is difficult to directly solve; it is transformed into the following two easily solved sub-problems: the multi-microgrid system profit maximization problem and the profit distribution sub-problem are as follows:
sub-problem 1: multi-microgrid system income maximization sub-problem
Figure FDA0003921821550000044
Sub-problem 2: profit sharing subproblems
Figure FDA0003921821550000045
In the formula (I), the compound is shown in the specification,
Figure FDA0003921821550000046
the optimal solution found in sub-problem 1;
4. solving a multi-microgrid cooperative operation optimization model based on an ADMM principle:
considering that the objective functions and constraint conditions of the two subproblems are non-convex, performing distributed solution by adopting an alternative direction multiplier method;
(1) Sub-problem 1 solution based on ADMM:
introducing Lagrange multiplier lambda ij And constructing an augmented Lagrangian function by using a penalty factor rho:
Figure FDA0003921821550000047
decomposing the formula (28) according to an ADMM algorithm principle to obtain a distributed optimization operation model of each microgrid; the method comprises the following steps of establishing a distributed algorithm of a benefit maximization subproblem of a multi-microgrid system, and specifically comprising the following steps:
(1) setting the maximum iteration times, wherein the convergence precision is 0.1, and the penalty factor rho =0.01;
(2) solving the distributed optimization model of each microgrid to obtain interactive electric power among the microgrids of each iteration;
(3) updating Lagrange multiplier:
Figure FDA0003921821550000051
(4) judging the convergence condition of the algorithm, if the convergence precision is met, terminating the iteration, otherwise, repeating the steps;
(2) Sub-problem 2 solution based on ADMM:
obtaining optimal expected transaction amount between micro grids by solving subproblem 1
Figure FDA0003921821550000052
Substituting into the electric energy trade profit maximization subproblem and introducing a Lagrange multiplier sigma ij And constructing an augmented Lagrangian function by using a penalty factor gamma:
Figure FDA0003921821550000053
decomposing the formula (29) according to an ADMM algorithm principle to obtain a distributed optimization operation model of each microgrid; here, the distributed algorithm is the same as sub-problem 1.
CN202211359585.7A 2022-11-02 2022-11-02 Multi-microgrid cooperative operation method based on data driving Pending CN115764863A (en)

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Cited By (2)

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Publication number Priority date Publication date Assignee Title
CN117811047A (en) * 2023-12-18 2024-04-02 南京东博智慧能源研究院有限公司 Electric energy optimization method of multi-microgrid comprehensive energy system containing carbon capture and electric conversion gas
CN117829538A (en) * 2024-01-09 2024-04-05 常州工程职业技术学院 Optimization method of energy hub network system considering fraud chess and multiple scenes

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117811047A (en) * 2023-12-18 2024-04-02 南京东博智慧能源研究院有限公司 Electric energy optimization method of multi-microgrid comprehensive energy system containing carbon capture and electric conversion gas
CN117829538A (en) * 2024-01-09 2024-04-05 常州工程职业技术学院 Optimization method of energy hub network system considering fraud chess and multiple scenes

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