CN113344733B - Optimization planning method and system for electrical interconnection comprehensive energy system - Google Patents

Optimization planning method and system for electrical interconnection comprehensive energy system Download PDF

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CN113344733B
CN113344733B CN202110462340.6A CN202110462340A CN113344733B CN 113344733 B CN113344733 B CN 113344733B CN 202110462340 A CN202110462340 A CN 202110462340A CN 113344733 B CN113344733 B CN 113344733B
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马千里
马志程
周强
杨贤明
邵冲
郭慧
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Abstract

The invention discloses an optimization planning method and a system for an electrical interconnection comprehensive energy system, wherein a comprehensive energy system optimization planning model combining an electric power system and a natural gas system is established, the model is a two-stage and three-level defense-attack-defense robust optimization planning model, and simultaneously comprises an extension plan of an electric power line and a natural gas pipeline; in the first tier, the system planning manager needs to rationally plan the power and gas systems jointly to minimize the maximum load loss due to any possible extreme events; at the second layer, an attacker can attack the most vulnerable parts in the power system and the natural gas system to cause the largest load loss; at the third level, the system operation manager optimizes the system internal operation state for extreme scenarios to achieve the minimum load loss in the damaged state. The invention lays a solid foundation for building a high-efficiency, continuous and safe modern energy supply system.

Description

Optimization planning method and system for electrical interconnection comprehensive energy system
Technical Field
The invention belongs to the technical field of optimization planning of an integrated energy system, and particularly relates to an optimization planning method and system of an electrical interconnection integrated energy system.
Background
An Integrated Energy System (IES) is an Energy production, supply and sale Integrated System which is formed by taking an electric power System as a core, highly coupling with a gas and a thermal System, and optimizing production, transmission, distribution, storage, conversion, consumption and other links of different Energy sources through unified coordination in a certain area range, and is a physical foundation of an Energy internet.
In the historical development process, the traditional energy system planning and construction are usually limited to the interior of a single energy supply form such as electricity, gas, cold and heat, mutual connection is lacked, and the advantages of different energy sources cannot be complemented. At present, the reserves of traditional fossil energy resources such as petroleum, coal and the like are reduced day by day, and the environmental pollution caused by the traditional fossil energy resources is treated at random, so that the existing mode of independent planning construction and operation among the original electric cold and heat energy supply systems is broken, the comprehensive planning operation is carried out to exert the advantage complementation of different energy resources, and finally, the establishment of a coordinated and unified social comprehensive energy system is paid more and more attention. Theoretically, the concept of the comprehensive energy system has been proposed decades ago, and in the development process of the social energy supply network, the complementary optimization of different energy forms exists for a long time, for example, a Combined Cooling Heating and Power (CCHP) unit improves the fuel utilization efficiency through recycling and coordinated scheduling between power supply and heat supply, so that the concept of the comprehensive energy system is proposed.
The regional comprehensive energy system is characterized in that fossil energy such as coal, petroleum and natural gas and new energy such as wind and light in a certain regional range are integrated, an electric power system with the most extensive energy utilization is taken as a core, and a gas system and a thermodynamic system are cooperatively and optimally operated together, so that the coupling between various energy systems with different properties is enhanced, the planning and operation targets are coordinated, the requirements of energy consumption ratio are improved, meanwhile, unified management and complementary mutual assistance are promoted, and the advantage complementation of resources in a multi-region multi-period multi-target form is fully exerted. The novel energy management mode not only can meet the requirements of diversified energy consumption such as social electric heating and cooling, but also greatly improves the energy utilization efficiency and reduces the environmental pollution, and is an effective mode for realizing sustainable development of energy.
The electricity-gas interconnection system is taken as a typical comprehensive energy system, and has received more and more attention and application with the vigorous promotion of low carbon, environmental protection, energy conservation and emission reduction in recent years. Compared with the traditional fossil energy sources such as coal, petroleum and the like, natural gas serving as a clean energy source has high combustion efficiency and less pollution emission, is easy to flow and is easy to couple with an electric power system and a thermodynamic system to form a comprehensive energy system with multiple energy complementation, and is also one of the best ways for realizing sustainable development. It has been intensively studied in more than 70 countries.
With the development of energy system construction, people pay more and more attention to the influence of extreme events on energy supply safety, and the extreme events comprise natural disasters, artificial attacks and the like, which are more frequent in recent years. In recent years, due to the limited reserves and serious pollution of traditional fossil energy, new energy power generation is more and more concerned, however, the electric energy quality and the power supply reliability of a power system are influenced by the fluctuation and intermittence of the output of new energy such as wind and light.
Because of the clean, efficient, high quality nature of natural gas as an energy source, certain regions now have natural gas systems combined with electrical power systems as the primary energy supply path. However, the operational stability and reliability of energy systems are being affected by more and more uncertain events, and the resulting confusion in energy systems is unavoidable and also uncontrollable and predictable. As a requirement of a new-age energy system, sustainability of energy supply must be ensured, and thus it is important to improve the level of resilience of the energy system. The resilience is defined as the capability of the system in resisting, adapting and rapidly recovering disturbance events, the disturbance events comprise various natural disasters, and as the global natural disasters gradually increase, the construction of an elastic power grid with sufficient resilience to cope with extreme events has become a common pursuit for the construction of energy systems of all countries in the world. At present, some researches have been carried out on the improvement of the restoring force in the field of micro-grids, but the research on the restoring force of a multi-energy-source coupled comprehensive energy system of electricity, gas, heat, cold and the like facing extreme events is not enough, the physical characteristics and the operating characteristics of a natural gas system are different from those of an electric power system and influence is caused on the level of the restoring force of the natural gas system, so that the research on the combined planning method of the electricity-gas interconnected comprehensive energy system has important significance on coping with the occurrence of the extreme events and improving the level of the restoring force of a modern energy supply system.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an optimization planning method and system for an electrical interconnection comprehensive energy system, which aim to overcome the defects in the prior art, solve a three-level two-stage robust optimization planning model by a column and constraint production algorithm, and provide a basis for planning in consideration of extreme scenes.
The invention adopts the following technical scheme:
an optimization planning method for an electrical interconnection comprehensive energy system comprises the following steps:
s1, obtaining system extension planning data, system operation constraint condition data and price data;
s2, taking the first-stage model as a main problem, and constructing a target function and a corresponding constraint condition of the main problem according to the system extension planning data, the system operation constraint condition data and the price data which are obtained in the step S1 to form a main problem overall model as an optimal planning model under the condition of not containing the influence of extreme events; solving an optimal planning model under the condition without the influence of extreme events to obtain an optimal planning scheme x;
s3, taking the second-stage model as a subproblem, constructing a subproblem objective function and a corresponding constraint condition according to the system operation constraint condition data obtained in the step S1 to form a subproblem overall model, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limit CL max As an iterative convergence condition, converting the double-layer problem of the sub-problem objective function into a single-layer problem by using the optimal planning scheme x obtained in the step S2, then solving a sub-problem overall model by using a commercial solver, and obtaining the worst extreme event scene alpha by considering a minimum load shedding optimization model influenced by the extreme event Iteration+1 Sum sub-problem objective function value
Figure BDA0003042754180000041
S4, updating the subproblem objective function value obtained in the step S3
Figure BDA0003042754180000042
Outputting the optimal planning scheme after the result is output and judged
Figure BDA00030427541800000410
And the worst extreme eventsScene alpha Iteration+1 And the optimization planning of the electrical interconnection comprehensive energy system is realized.
Specifically, in step S1, the system extension planning data includes: the method comprises the following steps of (1) power system grid structure data, power line parameter data to be built, natural gas system grid data, natural gas pipeline data to be built, gas turbine nodes to be built and model data;
the system operation constraint data comprises: the method comprises the following steps of (1) obtaining maximum power output data of a power grid, capacity data and power generation efficiency data of a gas turbine, power load and natural gas load data and natural gas source data;
the price data includes: the method comprises the following steps of power grid electricity price, natural gas price, price of a power line to be built, price of a natural gas pipeline to be built, price of a gas turbine to be built and annual equipment discount rate.
Specifically, in step S2, the main problem objective function is:
Figure BDA0003042754180000043
wherein the content of the first and second substances,
Figure BDA0003042754180000044
respectively representing the current breaking rate coefficients of the gas turbine, the power line and the natural gas pipeline;
Figure BDA0003042754180000045
the investment prices of corresponding units, lines and pipelines are respectively set;
Figure BDA0003042754180000046
the states of the corresponding units, lines and pipelines are respectively put into operation;
Figure BDA0003042754180000047
respectively the power grid electricity price and the gas price of the power grid at the typical day d and the moment t;
Figure BDA0003042754180000048
and
Figure BDA0003042754180000049
the electricity and gas purchased amount at the typical day d and time t are respectively.
Further, the main problem constraint conditions are specifically as follows:
equipment commissioning constraints are as follows:
Figure BDA0003042754180000051
Figure BDA0003042754180000052
Figure BDA0003042754180000053
the output of the power grid root node is constrained as follows:
Figure BDA0003042754180000054
the gas turbine output constraints are as follows:
Figure BDA0003042754180000055
the gas turbine gas-electricity constraints are as follows:
Figure BDA0003042754180000056
the node power balance constraint is as follows:
Figure BDA0003042754180000057
Figure BDA0003042754180000058
the line capacity constraints are as follows:
Figure BDA0003042754180000059
the output of the gas source is constrained as follows:
Figure BDA00030427541800000510
the gas system node flow balance constraint is as follows:
Figure BDA00030427541800000511
the gas pipeline flow constraints are as follows:
Figure BDA00030427541800000512
wherein gen is a gas turbine serial number index, pl is a power line serial number index, gp is a natural gas pipeline serial number index,
Figure BDA00030427541800000513
respectively corresponding to the initial commissioning states of the gas turbine, the line and the pipeline,
Figure BDA00030427541800000514
respectively establishing state variables of corresponding units, lines and pipelines;
Figure BDA00030427541800000515
is a variable of the output of the power grid,
Figure BDA0003042754180000061
is the maximum output of the root node of the power grid, n 0 Is a power grid root node;
Figure BDA0003042754180000062
respectively the output variable, the gas consumption variable and the gas-electricity conversion coefficient of the gas turbine,
Figure BDA0003042754180000063
is as follows;
Figure BDA0003042754180000064
is a node power load;
Figure BDA0003042754180000065
transmitting the electric quantity variable for the line;
Figure BDA0003042754180000066
output variable of gas source;
Figure BDA0003042754180000067
is a nodal natural gas load;
Figure BDA0003042754180000068
the natural gas flow variable is transmitted to the pipeline,
Figure BDA0003042754180000069
the power grid root node output is at t time within typical day d, f ( j ) For the starting node of the line to be j, t ( j ) The terminating node representing the line is j,
Figure BDA00030427541800000610
for the purpose of the maximum transmission capacity of the line,
Figure BDA00030427541800000611
for the transmission power of the line i at time t within a typical day d,
Figure BDA00030427541800000612
is the upper limit of the output of the natural gas source, gs is the index of the natural gas source number, gn is the index of the gas network node number, gp is the index of the natural gas pipeline number,
Figure BDA00030427541800000613
the maximum transmission capacity of the pipeline.
Specifically, in step S2, the overall model of the main problem is as follows:
Figure BDA00030427541800000614
s.t.Ax+By≤D
Figure BDA00030427541800000615
Figure BDA00030427541800000616
wherein, CL max To an upper limit of the maximum allowable load cut, a T For a constant matrix related to the variables of the project in the objective function of the main problem, b T Constant matrix related to operation variable in objective function of main problem, x is system project variable, y is system operation variable under normal operation state, A is constant coefficient matrix related to project variable under constraint condition under normal operation state, B is constant coefficient matrix related to operation variable under constraint condition under normal operation state, D is constant item matrix under constraint condition under normal operation state, c is constant item matrix under constraint condition T Is a constant coefficient related to the load shedding variable, k is the index of the worst extreme event scene number, iteration is the Iteration number and is the total number of the worst extreme event scene number,
Figure BDA00030427541800000617
is a constant coefficient matrix related to the input variable in the constraint condition under the extreme event state,
Figure BDA00030427541800000618
is a constant coefficient matrix, alpha, related to the operating variables in the constraint conditions under extreme event conditions k For the k-th extreme event,
Figure BDA00030427541800000619
a matrix of constant terms in constraints for extreme event conditions,
Figure BDA00030427541800000620
is the operation variable in the k-th extreme event scene.
Specifically, in step S3, the objective function of the sub-problem is as follows:
Figure BDA0003042754180000071
wherein the content of the first and second substances,
Figure BDA0003042754180000072
to plan the scheme
Figure BDA00030427541800000716
Alpha is an extreme event; phi is an extreme event scene set; y is a system operation variable;
Figure BDA0003042754180000073
is the system power cut load variable.
Further, the constraint conditions of the sub-problem are specifically:
attacking the uncertain ensemble constraints is as follows:
Figure BDA0003042754180000074
Figure BDA0003042754180000075
the output of the power grid root node is constrained as follows:
Figure BDA0003042754180000076
Figure BDA0003042754180000077
the gas turbine output constraints are as follows:
Figure BDA0003042754180000078
Figure BDA0003042754180000079
the gas turbine gas-electricity constraints are as follows:
Figure BDA00030427541800000710
Figure BDA00030427541800000711
the node power balance constraints are as follows:
Figure BDA00030427541800000712
Figure BDA00030427541800000713
the shear load constraints are as follows:
Figure BDA00030427541800000714
Figure BDA00030427541800000715
the line capacity constraints are as follows:
Figure BDA0003042754180000081
Figure BDA0003042754180000082
the output of the gas source is constrained as follows:
Figure BDA0003042754180000083
Figure BDA0003042754180000084
the gas system node flow balance constraint is as follows:
Figure BDA0003042754180000085
Figure BDA0003042754180000086
the gas pipeline flow constraints are as follows:
Figure BDA0003042754180000087
Figure BDA0003042754180000088
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003042754180000089
a state variable that is whether a line is corrupted,
Figure BDA00030427541800000810
as a lineK is the attack budget,
Figure BDA00030427541800000811
is the output variable of the power grid,
Figure BDA00030427541800000812
is the maximum output of the root node of the power grid, n 0 Is a power grid root node,
Figure BDA00030427541800000813
the state variables are built for the gas turbine, gen is the gas turbine number index,
Figure BDA00030427541800000814
is the output variable of the gas turbine,
Figure BDA00030427541800000815
is a variable of the gas consumption of the gas turbine,
Figure BDA00030427541800000816
is the gas-to-electricity conversion coefficient of the gas turbine,
Figure BDA00030427541800000817
in order to be a node electrical load,
Figure BDA00030427541800000818
in order to change the load-shedding variable,
Figure BDA00030427541800000819
for line transmission power variable, pl is the power line number index,
Figure BDA00030427541800000820
the maximum transmission capacity of the line, gs is the natural gas source number index,
Figure BDA00030427541800000821
is the upper limit of the output of the natural gas source,
Figure BDA00030427541800000822
is a variable output of a natural gas source,
Figure BDA00030427541800000823
in order to be the natural gas load of the node,
Figure BDA00030427541800000824
the natural gas flow variable is transmitted to the pipeline,
Figure BDA00030427541800000825
in order to maximize the transmission capacity of the pipeline,
Figure BDA00030427541800000826
a state variable is built for the commissioning of the pipeline,
Figure BDA00030427541800000827
is a load shedding variable;
Figure BDA00030427541800000828
is a line attack variable;
Figure BDA00030427541800000829
lagrange dual variables corresponding to the respective 11 constraints.
Specifically, in step S3, the overall subproblem model:
Figure BDA00030427541800000830
Figure BDA00030427541800000831
wherein the content of the first and second substances,
Figure BDA0003042754180000091
to plan the scheme
Figure BDA0003042754180000092
The value of the objective function of the following,
Figure BDA0003042754180000093
for system operating variables in extreme event states, c T For constant coefficients related to the tangential load variable,. Psi.
Figure BDA0003042754180000094
In all the possible extreme scenarios that follow,
Figure BDA0003042754180000095
is a constant coefficient matrix related to the input variable in the constraint condition under the extreme event state,
Figure BDA0003042754180000096
the optimal planning solution obtained for the solution of the main problem,
Figure BDA0003042754180000097
is a constant coefficient matrix related to an operation variable in a constraint condition under an extreme event state, C is a constant coefficient matrix related to an attack variable in the constraint condition under the extreme event state, alpha is an extreme event scene,
Figure BDA0003042754180000098
is a matrix of constant terms in the constraints of the extreme event state.
Specifically, step S4 specifically includes:
s401, if UB is less than or equal to CL max UB is the maximum load loss, CL, corresponding to the optimal planning scheme updated each iteration max For the maximum allowable load shedding upper limit, the optimal planning scheme
Figure BDA0003042754180000099
Meet the resilience requirement under any extreme event scene, so the planning scheme is returned
Figure BDA00030427541800000910
And terminating the iteration;
s402, e.g.Fruit UB is greater than or equal to CL max Creating new variables
Figure BDA00030427541800000911
Establishing corresponding constraints corresponding to the sub-problems and adding the constraints into the main problem;
s403, update operation = operation +1, and the process returns to step S3.
Another technical solution of the present invention is an electrical interconnection comprehensive energy system optimization planning system, including:
the data module is used for acquiring system extension planning data, system operation constraint condition data and price data;
the first solving module is used for taking the first-stage model as a main problem, constructing a target function of the main problem and corresponding constraint conditions according to system extension planning data, system operation constraint condition data and price data acquired by the data module, and forming a main problem overall model as an optimal planning model under the condition of not containing extreme event influence; solving an optimal planning model under the condition without the influence of extreme events to obtain an optimal planning scheme x;
the second solving module is used for taking the second-stage model as a subproblem, constructing a subproblem objective function and a corresponding constraint condition according to the system operation constraint condition data acquired by the data module to form a subproblem overall model, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limit CL max As an iterative convergence condition, converting a double-layer problem of a sub-problem objective function into a single-layer problem by using an optimal planning scheme x obtained by a first solving module, then solving a sub-problem overall model by using a commercial solver, and obtaining the worst extreme event scene alpha by considering a minimum load shedding optimization model of the influence of the extreme event Iteration+1 Sum sub-problem objective function value
Figure BDA0003042754180000101
A planning module for updating the sub-problem objective function value obtained by the second solving module
Figure BDA0003042754180000102
Outputting the optimal planning scheme after the result is output and judged
Figure BDA0003042754180000103
And the worst extreme event scenario alpha Iteration+1 And the optimization planning of the electrical interconnection comprehensive energy system is realized.
Compared with the prior art, the invention at least has the following beneficial effects:
the invention relates to an optimization planning method for an electrical interconnection comprehensive energy system, which is used for optimizing an electrical interconnection comprehensive energy system planning model considering extreme events in a conventional mode aiming at a specific extreme scene or enumerating different scenes to determine the worst scene. The two-stage three-level robust planning model provided by the patent can determine the worst extreme event scene aiming at different planning schemes, and can add the corresponding scene into the first-stage planning model to ensure that the new planning scheme can resist the extreme scene certainly, thereby avoiding the result of repeated planning; the invention establishes a comprehensive energy system optimization planning model combining an electric power system and a natural gas system, which is a two-stage and three-level 'defending-attacking-defending' robust optimization planning model and simultaneously comprises an expansion plan of an electric power line and a natural gas pipeline.
Furthermore, data are obtained through the step S1, and a corresponding two-stage robust optimization model is established according to different application scenes, so that the applicability of the model is improved.
Furthermore, the main objective function is set from the economic perspective, the minimum sum of the total investment cost and the operation cost is taken as the objective function, and the social and economic development requirements are met.
Furthermore, the main problem constraint condition setting is to construct a complete model to solve, and meanwhile, the constraint conditions consider the limitations of the gas turbine, the power line and the natural gas pipeline such as the commissioning state, the power generation capacity, the efficiency and the transmission capacity, so that the method better conforms to the practical application scene, and the obtained result has better accuracy and practicability.
Furthermore, the main problem overall model is set for clearly showing the problem structure, the form is concise, readers can conveniently understand and write programs, and meanwhile, a foundation is laid for the subsequent iteration process.
Further, a double-layer objective function is established in the subproblem, the outer-layer max objective is to find the worst scenario from all extreme event scenarios, the inner-layer min objective is to optimize the operation of the system itself to minimize the load loss, and the worst extreme event and the maximum load loss in all scenarios can be found by combining the two objectives.
Furthermore, the sub-problem constraint condition setting is to construct a complete model for solving, and the constraint conditions consider the planning and construction state, the power generation capacity and efficiency, the transmission capacity, the load shedding capacity, the attack budget and other limitations of the gas turbine, the power line and the natural gas pipeline, so that the accident state can be simulated really, and the obtained result has higher practicability.
Furthermore, the overall sub-problem model is set for clearly showing the problem structure, the form is concise, readers can conveniently understand and write programs, and meanwhile, a foundation is laid for the iteration process.
Further, the step S4 is set to record the intermediate variables in the iterative process and determine the convergence condition, so that a planning and extension scheme just meeting the maximum load loss can be found out, and the investment cost is minimized while meeting the demand of system resilience.
In conclusion, the invention can show that the combined planning of the electric-gas interconnected system can effectively improve the resilience level of the comprehensive energy system according to the actual result, can provide a planning scheme with optimal economy under different resilience index requirements, and lays a solid foundation for constructing a high-efficiency, continuous and safe modern energy supply system.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a network topology diagram of the present invention, wherein (a) is a 37-node grid and (b) is a 20-node gas grid.
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 some, not all, embodiments of the present invention. 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.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
The invention provides an optimization planning method for an electrical interconnection comprehensive energy system, which establishes a combined comprehensive energy system optimization planning model of an electric power system and a natural gas system, wherein the model is a two-stage three-level 'resist-attack-resist' robust optimization planning model and simultaneously comprises an extension plan of an electric power line and a natural gas pipeline. In the first tier, the system planning manager needs to rationally plan the power and gas systems jointly to minimize the maximum load loss due to any possible extreme events; in the second layer, the attacker attacks the most vulnerable parts of the power system and the natural gas system to cause the largest load loss; in the last third layer, the system operation manager optimizes the system internal operation state for the extreme scenario, and the minimum load loss under the damaged state is achieved. In order to solve the three-layer robust model which is difficult to solve directly by using a solver, the invention adopts a column-and-constraint generation algorithm (CCG) to solve the three-layer robust model, the algorithm is mainly used for finding the worst extreme scenes under different planning schemes, corresponding constraints are added into the original problem, and the feasible domain range can be continuously reduced until the algorithm converges with the increase of the scenes and the number of the constraints.
Referring to fig. 1, the method for optimizing and planning an electrical interconnection integrated energy system of the present invention includes the following steps:
s1, obtaining system extension planning data, system operation constraint condition data and price data;
system extension planning data: the grid structure data of the power system and the parameter data of the power line to be built. The method comprises the steps of natural gas system net rack data, natural gas pipeline data to be built, gas turbine nodes to be built and model data.
System operation constraint data: the system comprises power grid maximum output data, gas turbine capacity data, power generation efficiency data, power load and natural gas load data and natural gas source data.
Price data; the method comprises the following steps of power grid electricity price, natural gas price, price of a power line to be built, price of a natural gas pipeline to be built, price of a gas turbine to be built and annual equipment discount rate.
S2, taking a first-stage planning stage model as a main problem, and constructing a target function and corresponding constraint conditions of the main problem to form a main problem overall model; the first time is to solve the optimal planning model under the condition without the influence of extreme events, and an optimal planning scheme x is obtained after the solution, and the optimal planning scheme x is used
Figure BDA00030427541800001411
Marking;
first stage objective function: the planning stage aims at minimizing the total investment cost, and the total cost mainly comprises two parts of equipment investment cost and operation consumption cost;
constructing a main problem objective function:
Figure BDA0003042754180000141
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003042754180000142
respectively representing the current breaking rate coefficients of the gas turbine, the power line and the natural gas pipeline;
Figure BDA0003042754180000143
the investment prices of corresponding units, lines and pipelines are respectively set;
Figure BDA0003042754180000144
the states of the corresponding units, lines and pipelines are respectively built;
Figure BDA0003042754180000145
respectively at time t on typical day dGrid electricity prices and grid gas prices;
Figure BDA0003042754180000146
and
Figure BDA0003042754180000147
the electricity and gas purchased amount at the typical day d and time t are respectively.
And constructing main problem constraint conditions, including equipment commissioning, equipment self-constraint, power flow balance and other constraints.
Equipment commissioning constraints:
Figure BDA0003042754180000148
power grid root node output constraint:
Figure BDA0003042754180000149
gas turbine output constraint:
Figure BDA00030427541800001410
gas turbine gas-electric constraint:
Figure BDA0003042754180000151
node power balance constraint:
Figure BDA0003042754180000152
and (3) line capacity constraint:
Figure BDA0003042754180000153
output restraint of a gas source:
Figure BDA0003042754180000154
and (3) node flow balance constraint of a gas system:
Figure BDA0003042754180000155
and (3) restricting the flow of the gas pipeline:
Figure BDA0003042754180000156
wherein the content of the first and second substances,
Figure BDA0003042754180000157
respectively establishing state variables of corresponding units, lines and pipelines;
Figure BDA0003042754180000158
the output variable of the power grid is taken as the output variable of the power grid;
Figure BDA0003042754180000159
respectively representing the output variable, the gas consumption variable and the gas-electricity conversion coefficient of the gas turbine;
Figure BDA00030427541800001510
is a node power load;
Figure BDA00030427541800001511
transmitting the electric quantity variable for the line;
Figure BDA00030427541800001512
output variable of gas source;
Figure BDA00030427541800001513
is a nodal natural gas load;
Figure BDA00030427541800001514
the natural gas flow variable is transmitted to the pipeline.
Forming a main problem overall model:
Figure BDA00030427541800001515
s.t.Ax+By≤D
Figure BDA00030427541800001516
Figure BDA00030427541800001517
wherein, CL max In order to achieve the maximum allowable load shedding upper limit, the main problem model is a mixed integer linear programming Model (MILP), and can be efficiently solved by directly using a commercial solver (such as cplex, gurobi and the like); after solving, an optimal planning scheme x can be obtained, and the optimal planning scheme x is used
Figure BDA0003042754180000167
And (6) marking.
S3, taking the second stage, namely the resilience stage, as a subproblem, constructing a subproblem objective function and a corresponding constraint condition, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limit CL max Forming a subproblem overall model as an iterative convergence condition, converting the double-layer problem of the subproblem objective function into a single-layer problem by using the optimal planning scheme x obtained in the step S3, and then solving the subproblem overall model by using a commercial solver to obtain the worst extreme event scene alpha Iteration+1 Sum sub-problem objective function value
Figure BDA0003042754180000161
Objective function of sub-problem: the main goal of the system in the resilience phase is to find the worst extreme event scenario based on the commissioning scheme given in the planning phase and to minimize the load loss through the system optimization operation.
The objective function of the sub-problem is a two-layer, as follows:
Figure BDA0003042754180000162
wherein the content of the first and second substances,
Figure BDA0003042754180000163
to plan the scheme
Figure BDA0003042754180000168
The value of the objective function, alpha, is an extreme event; phi is an extreme event scene set; y is a system operation variable;
Figure BDA0003042754180000164
and the system power cut load variable is obtained.
Constructing a subproblem constraint condition:
the subproblem constraint condition mainly refers to a constraint condition under an extreme event, does not contain a commissioning constraint, increases a loss load constraint and a constraint considering a line fault, and uses-marks variables of the stage when the other constraints are consistent with the previous stage.
Attack uncertainty set constraints:
Figure BDA0003042754180000165
power grid root node output constraint:
Figure BDA0003042754180000166
gas turbine output constraint:
Figure BDA0003042754180000171
gas turbine gas-electric constraint:
Figure BDA0003042754180000172
node power balance constraint:
Figure BDA0003042754180000173
load shedding restraint:
Figure BDA0003042754180000174
and (3) line capacity constraint:
Figure BDA0003042754180000175
gas source output constraint:
Figure BDA0003042754180000176
and (3) node flow balance constraint of the gas system:
Figure BDA0003042754180000177
and (3) restricting the flow of the gas pipeline:
Figure BDA0003042754180000178
wherein most variables are consistent with the planning stage and are marked by using;
Figure BDA0003042754180000179
is a load shedding variable;
Figure BDA00030427541800001710
is a line attack variable; k is the attack budget;
Figure BDA00030427541800001711
lagrange dual variables corresponding to the respective 11 constraints.
Forming a subproblem overall model:
Figure BDA0003042754180000181
Figure BDA0003042754180000182
finally, the robust planning models in the first stage and the second stage are obtained as follows:
Figure BDA0003042754180000183
optimal planning scheme given in main problem
Figure BDA0003042754180000189
Solving the subproblems on the basis, because the objective function of the subproblems is a max-min double-layer function and cannot be directly solved, the double-layer problem needs to be converted into a single-layer problem by applying a strong dual principle, and then a commercial solver (such as cplex, gurobi and the like) is used for efficiently solving the problem. Obtaining the worst extreme event scene alpha Iteration+1 Sum sub-problem objective function value
Figure BDA0003042754180000184
S4, updating the subproblem objective function value obtained in the step S3
Figure BDA0003042754180000185
And the result is output and judged, and the optimal planning scheme is output
Figure BDA00030427541800001810
The Iteration times Iteraction =0 is set for marking each planning scheme and corresponding maximum load loss in the display calculation process, and represents the extreme scene constraint quantity needing to be added into the main problem; and the maximum load shedding UB = + ∞ is used for recording the maximum load loss corresponding to the optimal planning scheme obtained by solving the main problem each time, namely a sub-problem objective function.
S401, if UB is less than or equal to CL max Optimal planning plan at this time
Figure BDA0003042754180000186
Meet the resilience requirement under any extreme event scene, so the planning scheme is returned
Figure BDA0003042754180000187
And terminating the iteration;
s402, if UB is more than or equal to CL max Creating new variables
Figure BDA0003042754180000188
And establishing corresponding constraints corresponding to the sub-problems to be added into the main problem as follows:
Figure BDA0003042754180000191
Figure BDA0003042754180000192
the constraint condition of the type can ensure that a new planning scheme obtained by solving the main problem is necessarily satisfied in the worst extreme event scene alpha Iteration+1 Maximum load cut under.
S403, update operation = operation +1, and the process returns to step S3.
In another embodiment of the present invention, an optimized planning system for an electrical interconnection integrated energy system is provided, where the system can be used to implement the above optimized planning method for an electrical interconnection integrated energy system, and specifically, the optimized planning system for an electrical interconnection integrated energy system includes a data module, a first solving module, a second solving module, and a planning module.
The data module is used for acquiring system extension planning data, system operation constraint condition data and price data;
the first solving module is used for taking the first-stage model as a main problem, constructing a target function of the main problem and corresponding constraint conditions according to system extension planning data, system operation constraint condition data and price data acquired by the data module, and forming a main problem overall model as an optimal planning model under the condition of not containing extreme event influence; solving an optimal planning model under the condition without the influence of extreme events to obtain an optimal planning scheme x;
the second solving module is used for taking the second-stage model as a subproblem, constructing a subproblem objective function and a corresponding constraint condition according to the system operation constraint condition data acquired by the data module to form a subproblem overall model, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limit CL max As an iterative convergence condition, converting the double-layer problem of the sub-problem objective function into a single-layer problem by using the optimal planning scheme x obtained by the first solving module, then solving a sub-problem overall model by using a commercial solver, and obtaining the worst extreme event scene alpha by considering a minimum load shedding optimization model influenced by the extreme event Iteration+1 Sum sub-problem objective function value
Figure BDA0003042754180000193
A planning module for updating the sub-problem objective function value obtained by the second solving module
Figure BDA0003042754180000194
Outputting the optimal planning scheme after the result is output and judged
Figure BDA0003042754180000195
And the worst extreme event scenario alpha Iteration+1 Realize electricityAnd optimizing and planning the gas interconnection comprehensive energy system.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the optimization planning method of the electrical interconnection comprehensive energy system, and comprises the following steps:
acquiring system extension planning data, system operation constraint condition data and price data; taking the first-stage model as a main problem, constructing a target function of the main problem and a corresponding constraint condition according to the obtained system extension planning data, system operation constraint condition data and price data, and forming a main problem overall model as an optimal planning model without extreme event influence; solving an optimal planning model under the condition without the influence of extreme events to obtain an optimal planning scheme x; taking the second stage model as a subproblem, constructing a subproblem objective function and a corresponding constraint condition according to the obtained system operation constraint condition data to form a subproblem overall model, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limit CL max As an iterative convergence condition, converting the double-layer problem of the sub-problem objective function into a single-layer problem by using the obtained optimal planning scheme x, then solving a sub-problem overall model by using a commercial solver, and obtaining the worst minimum load shedding optimization model by considering the influence of extreme eventsExtreme event scenario alpha Iteration+1 Sum sub-problem objective function value
Figure BDA0003042754180000201
Updated subproblem objective function values
Figure BDA0003042754180000202
Outputting the optimal planning scheme after the result is output and judged
Figure BDA0003042754180000203
And the worst extreme event scenario α Iteration+1 And the optimization planning of the electrical interconnection comprehensive energy system is realized.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor can load and execute one or more instructions stored in the computer readable storage medium to realize the corresponding steps of the electrical interconnection comprehensive energy system optimization planning method in the embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
acquiring system extension planning data, system operation constraint condition data and price data; modeling the first stageAs a main problem, constructing a target function of the main problem and a corresponding constraint condition according to the acquired system extension planning data, system operation constraint condition data and price data, and forming a main problem overall model as an optimal planning model without the influence of extreme events; solving the optimal planning model without the influence of the extreme event to obtain an optimal planning scheme x; taking the second stage model as a subproblem, constructing a subproblem objective function and a corresponding constraint condition according to the obtained system operation constraint condition data to form a subproblem overall model, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limit CL max As an iterative convergence condition, converting the double-layer problem of the sub-problem objective function into a single-layer problem by using the obtained optimal planning scheme x, then solving a sub-problem overall model by using a commercial solver, and obtaining the worst extreme event scene alpha by considering a minimum load shedding optimization model influenced by the extreme event Iteration+1 Sum sub-problem objective function value
Figure BDA0003042754180000221
Updating the obtained subproblem objective function values
Figure BDA0003042754180000222
Outputting the optimal planning scheme after the result is output and judged
Figure BDA0003042754180000223
And the worst extreme event scenario alpha Iteration+1 And the optimization planning of the electrical interconnection comprehensive energy system is realized.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
Referring to fig. 2, for an improved electrical-gas interconnection system composed of an IEEE37 node power system and a belgium 20 node natural gas system, the peak load of the power grid is 9872kW.
The table shows the parameters of the gas turbine to be built and the power line to be built:
TABLE 1 gas turbine to be built parameter Table
Figure BDA0003042754180000224
Figure BDA0003042754180000231
TABLE 2 parameter table of power line to be built
Numbering Starting node Termination node Capacity/kW Cost/ten thousand yuan
PL1
37 12 1500 200
PL2 34 22 1500 200
PL3 21 25 1500 200
PL4 25 29 1500 200
PL5 28 30 1500 200
PL6 15 24 1500 200
PL7 24 32 1500 200
PL8 19 33 1500 200
PL9 13 18 1500 200
For the above example parameters, a comparison scheme with different resilience indexes and attack budgets is set, as shown in table 3:
table 3 comparative case settings
Figure BDA0003042754180000232
Figure BDA0003042754180000241
Inputting the corresponding case parameters into the proposed planning model for programming solution, and obtaining the corresponding optimal planning scheme as shown in table 4:
table 4 comparative case planning results
Case numbering Gas turbine Power line Total investment cost/ten thousand yuan Maximum load loss/kWh
1 G2,G3,G7,G9,G11 PL3,PL8 7469.7 17069
2 G1,G5,G7,G9,G10 PL3,PL7 7469.7 13042
3 G1,G5,G7,G9,G10 PL2,PL3,PL6,PL8 7507.5 8352
4 G2,G5,G7,G8,G11 PL2,PL3 7493.3 28640
5 G1,G4,G7,G8,G11 PL2,PL3,PL6,PL7,PL8 7526.3 19596
As can be seen from the table, for different resilience indexes and attack variables, the provided two-stage three-level robust optimization planning model can provide the most economic planning scheme meeting the resilience requirement for different cases, and the effectiveness and the rationality of the model are verified.
In conclusion, the optimization planning method for the electrical interconnection comprehensive energy system can show that the restoration force level of the comprehensive energy system can be effectively improved through the combined planning of the electrical interconnection system and the electrical interconnection system according to the actual result, can provide a planning scheme with optimal economy under different restoration force index requirements, and lays a solid foundation for building an efficient, continuous and safe modern energy supply system. .
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. An optimization planning method for an electrical interconnection comprehensive energy system is characterized by comprising the following steps:
s1, obtaining system extension planning data, system operation constraint condition data and price data;
s2, taking the first-stage model as a main problem, and constructing a target function and a corresponding constraint condition of the main problem according to the system extension planning data, the system operation constraint condition data and the price data which are obtained in the step S1 to form a main problem overall model as an optimal planning model under the condition of not containing the influence of extreme events; solving the optimal planning model without the influence of the extreme event to obtain an optimal planning scheme x;
s3, taking the second-stage model as a subproblem, constructing a subproblem objective function and a corresponding constraint condition according to the system operation constraint condition data obtained in the step S1 to form a subproblem overall model, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limitCL max As an iterative convergence condition, converting the double-layer problem of the sub-problem objective function into a single-layer problem by using the optimal planning scheme x obtained in the step S2, then solving a sub-problem overall model by using a commercial solver, and obtaining the worst extreme event scene alpha by considering a minimum load shedding optimization model influenced by the extreme event Iteration+1 Sum sub-problem objective function value
Figure FDA0003042754170000011
S4, updating the subproblem objective function value obtained in the step S3
Figure FDA0003042754170000012
Outputting the optimal planning scheme after the result is output and judged
Figure FDA0003042754170000013
And the worst extreme event scenario alpha Iteration+1 And the optimization planning of the electrical interconnection comprehensive energy system is realized.
2. The method according to claim 1, wherein in step S1, the system extension planning data comprises: the method comprises the following steps of (1) power system grid structure data, power line parameter data to be built, natural gas system grid data, natural gas pipeline data to be built, gas turbine nodes to be built and model data;
the system operation constraint data comprises: the method comprises the following steps of (1) obtaining maximum power output data of a power grid, capacity data and power generation efficiency data of a gas turbine, power load and natural gas load data and natural gas source data;
the price data includes: the method comprises the following steps of power grid electricity price, natural gas price, price of a power line to be built, price of a natural gas pipeline to be built, price of a gas turbine to be built and annual equipment discount rate.
3. The method of claim 1, wherein in step S2, the main problem objective function is:
Figure FDA0003042754170000021
wherein the content of the first and second substances,
Figure FDA0003042754170000022
respectively representing the current breaking rate coefficients of the gas turbine, the power line and the natural gas pipeline;
Figure FDA0003042754170000023
the investment prices of corresponding units, lines and pipelines are respectively set;
Figure FDA0003042754170000024
the states of the corresponding units, lines and pipelines are respectively put into operation;
Figure FDA0003042754170000025
respectively the power grid electricity price and the gas price of the power grid at the typical day d and the moment t;
Figure FDA0003042754170000026
and
Figure FDA0003042754170000027
the electricity and gas purchased amount at the typical day d and time t are respectively.
4. The method according to claim 3, characterized in that the main problem constraints are in particular:
equipment commissioning constraints are as follows:
Figure FDA0003042754170000028
Figure FDA0003042754170000029
Figure FDA00030427541700000210
the output of the power grid root node is constrained as follows:
Figure FDA00030427541700000211
the gas turbine output constraints are as follows:
Figure FDA00030427541700000212
the gas turbine gas-electricity constraints are as follows:
Figure FDA00030427541700000213
the node power balance constraint is as follows:
Figure FDA00030427541700000214
Figure FDA00030427541700000215
the line capacity constraints are as follows:
Figure FDA0003042754170000031
the output of the gas source is constrained as follows:
Figure FDA0003042754170000032
the gas system node flow balance constraint is as follows:
Figure FDA0003042754170000033
the gas pipeline flow constraints are as follows:
Figure FDA0003042754170000034
wherein gen is a gas turbine serial number index, pl is a power line serial number index, gp is a natural gas pipeline serial number index,
Figure FDA0003042754170000035
respectively corresponding to the initial commissioning states of the gas turbine, the line and the pipeline,
Figure FDA0003042754170000036
respectively establishing state variables for corresponding units, lines and pipelines;
Figure FDA0003042754170000037
is a variable of the output of the power grid,
Figure FDA0003042754170000038
maximum output, n, for the grid root node 0 Is a power grid root node;
Figure FDA0003042754170000039
respectively the output variable, the gas consumption variable and the gas-electricity conversion coefficient of the gas turbine,
Figure FDA00030427541700000310
is as follows;
Figure FDA00030427541700000311
is a node power load;
Figure FDA00030427541700000312
transmitting the electric quantity variable for the line;
Figure FDA00030427541700000313
output variables for gas sources;
Figure FDA00030427541700000314
is a nodal natural gas load;
Figure FDA00030427541700000315
the natural gas flow variable is transmitted to the pipeline,
Figure FDA00030427541700000316
the power grid root node output at the time t in a typical day d, f (j) is the initial node of the line and is j, t (j) represents the termination node of the line and is j,
Figure FDA00030427541700000317
for the purpose of the maximum transmission capacity of the line,
Figure FDA00030427541700000318
for the transmission power of the line i at time t within a typical day d,
Figure FDA00030427541700000319
is the upper limit of the output of the natural gas source, gs is the index of the natural gas source number, gn is the index of the gas network node number, gp is the index of the natural gas pipeline number,
Figure FDA00030427541700000320
the maximum transmission capacity of the pipeline.
5. The method of claim 1, wherein in step S2, the master problem population model is as follows:
Figure FDA0003042754170000041
s.t.Ax+By≤D
Figure FDA0003042754170000042
Figure FDA0003042754170000043
wherein, CL max To an upper limit of the maximum allowable load cut, a T For a constant matrix related to the variables of the project in the objective function of the main problem, b T Constant matrix related to operation variable in objective function of main problem, x is system project variable, y is system operation variable under normal operation state, A is constant coefficient matrix related to project variable under constraint condition under normal operation state, B is constant coefficient matrix related to operation variable under constraint condition under normal operation state, D is constant item matrix under constraint condition under normal operation state, c is constant item matrix under constraint condition T Is a constant coefficient related to the load shedding variable, k is the index of the worst extreme event scene number, iteration is the Iteration number, and is the total number of the worst extreme event scene number,
Figure FDA0003042754170000044
a constant coefficient matrix related to the set-up variable in the constraint condition under the extreme event state,
Figure FDA0003042754170000045
is a constant coefficient matrix, alpha, related to the operating variable in the constraint conditions under extreme event conditions k For the k-th extreme event,
Figure FDA0003042754170000046
a matrix of constant terms in constraints for extreme event conditions,
Figure FDA0003042754170000047
is the operation variable in the k-th extreme event scene.
6. The method of claim 1, wherein in step S3, the objective function of the sub-problem is as follows:
Figure FDA0003042754170000048
wherein the content of the first and second substances,
Figure FDA0003042754170000049
for planning a plan
Figure FDA00030427541700000410
Alpha is an extreme event; phi is an extreme event scene set; y is a system operation variable;
Figure FDA00030427541700000411
is the system power cut load variable.
7. The method according to claim 6, wherein the constraints of the sub-questions are specifically:
attacking the uncertain ensemble constraints is as follows:
Figure FDA00030427541700000412
Figure FDA00030427541700000413
the output of the power grid root node is constrained as follows:
Figure FDA0003042754170000051
Figure FDA0003042754170000052
the gas turbine output constraints are as follows:
Figure FDA0003042754170000053
Figure FDA0003042754170000054
the gas turbine has the following gas-electric constraints:
Figure FDA0003042754170000055
Figure FDA0003042754170000056
the node power balance constraint is as follows:
Figure FDA0003042754170000057
Figure FDA0003042754170000058
the shear load constraints are as follows:
Figure FDA0003042754170000059
Figure FDA00030427541700000510
the line capacity constraints are as follows:
Figure FDA00030427541700000511
Figure FDA00030427541700000512
the output of the gas source is constrained as follows:
Figure FDA00030427541700000513
Figure FDA00030427541700000514
the gas system node flow balance constraint is as follows:
Figure FDA00030427541700000515
Figure FDA00030427541700000516
the gas pipeline flow constraints are as follows:
Figure FDA0003042754170000061
Figure FDA0003042754170000062
wherein the content of the first and second substances,
Figure FDA0003042754170000063
a state variable that is whether a line is corrupted,
Figure FDA0003042754170000064
a state variable is built for the line, k is the attack budget,
Figure FDA0003042754170000065
is the output variable of the power grid,
Figure FDA0003042754170000066
is the maximum output of the root node of the power grid, n 0 Is a power grid root node,
Figure FDA0003042754170000067
the state variables are built for the gas turbine, gen is the gas turbine number index,
Figure FDA0003042754170000068
is the output variable of the gas turbine,
Figure FDA0003042754170000069
is a variable of the gas consumption of the gas turbine,
Figure FDA00030427541700000610
is the gas-to-electricity conversion coefficient of the gas turbine,
Figure FDA00030427541700000611
in order to be a node electrical load,
Figure FDA00030427541700000612
in order to cut the load variable,
Figure FDA00030427541700000613
for line transmission power variable, pl is the power line number index,
Figure FDA00030427541700000614
the maximum transmission capacity of the line, gs is the natural gas source number index,
Figure FDA00030427541700000615
is the upper limit of the output of the natural gas source,
Figure FDA00030427541700000616
is a variable output of a natural gas source,
Figure FDA00030427541700000617
in order to be the natural gas load of the node,
Figure FDA00030427541700000618
to transmit the natural gas flow variable for the pipeline,
Figure FDA00030427541700000619
in order to maximize the transmission capacity of the pipeline,
Figure FDA00030427541700000620
a state variable is built for the commissioning of the pipeline,
Figure FDA00030427541700000621
is a load shedding variable;
Figure FDA00030427541700000622
is a line attack variable;
Figure FDA00030427541700000623
lagrange dual variables corresponding to the respective 11 constraints.
8. The method according to claim 1, wherein in step S3, the overall model of the sub-problem:
Figure FDA00030427541700000624
Figure FDA00030427541700000625
wherein the content of the first and second substances,
Figure FDA00030427541700000626
to plan the scheme
Figure FDA00030427541700000627
The value of the objective function of the following,
Figure FDA00030427541700000628
for system operating variables in extreme event states, c T For constant coefficients related to the tangential load variables,. Psi.
Figure FDA00030427541700000629
In all the possible extreme scenarios that follow,
Figure FDA00030427541700000630
is a constant coefficient matrix related to the input variable in the constraint condition under the extreme event state,
Figure FDA00030427541700000631
the optimal planning solution obtained for the solution of the main problem,
Figure FDA00030427541700000632
is a constant coefficient matrix related to an operation variable in a constraint condition under an extreme event state, C is a constant coefficient matrix related to an attack variable in the constraint condition under the extreme event state, alpha is an extreme event scene,
Figure FDA00030427541700000633
is a matrix of constant terms in the constraints of the extreme event state.
9. The method according to claim 1, wherein step S4 is specifically:
s401, if UB is less than or equal to CL max UB is the maximum load loss, CL, corresponding to the optimal planning scheme updated each iteration max For the maximum allowable load shedding upper limit, the optimal planning scheme
Figure FDA0003042754170000071
Meet the resilience requirement under any extreme event scene, so the planning scheme is returned
Figure FDA0003042754170000072
And terminating the iteration;
s402, if UB is more than or equal to CL max Creating new variables
Figure FDA0003042754170000073
Establishing corresponding constraints corresponding to the sub-problems and adding the constraints into the main problem;
s403, update operation = operation +1, and the process returns to step S3.
10. An electrical interconnection comprehensive energy system optimization planning system is characterized by comprising:
the data module is used for acquiring system extension planning data, system operation constraint condition data and price data;
the first solving module is used for taking the first-stage model as a main problem, constructing a target function of the main problem and corresponding constraint conditions according to system extension planning data, system operation constraint condition data and price data acquired by the data module, and forming a main problem overall model as an optimal planning model under the condition of not containing extreme event influence; solving an optimal planning model under the condition without the influence of extreme events to obtain an optimal planning scheme x;
the second solving module is used for taking the second-stage model as a subproblem, constructing a subproblem objective function and a corresponding constraint condition according to the system operation constraint condition data acquired by the data module to form a subproblem overall model, and enabling the maximum value of the subproblem objective function to be smaller than the maximum load shedding upper limit CL max As an iterative convergence condition, converting a double-layer problem of a sub-problem objective function into a single-layer problem by using an optimal planning scheme x obtained by a first solving module, then solving a sub-problem overall model by using a commercial solver, and obtaining the worst extreme event scene alpha by considering a minimum load shedding optimization model of the influence of the extreme event Iteration+1 Sum sub-problem objective function value
Figure FDA0003042754170000074
A planning module for updating the sub-problem objective function value obtained by the second solving module
Figure FDA0003042754170000075
Outputting the optimal planning scheme after the result is output and judged
Figure FDA0003042754170000076
And the worst extreme event scenario alpha Iteration+1 And the optimization planning of the electrical interconnection comprehensive energy system is realized.
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