CN115545340A - Comprehensive energy station-network collaborative planning method considering double uncertainties - Google Patents

Comprehensive energy station-network collaborative planning method considering double uncertainties Download PDF

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CN115545340A
CN115545340A CN202211347153.4A CN202211347153A CN115545340A CN 115545340 A CN115545340 A CN 115545340A CN 202211347153 A CN202211347153 A CN 202211347153A CN 115545340 A CN115545340 A CN 115545340A
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于模江
曾平良
刘佳
李亚楼
唐早
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Hangzhou Dianzi University
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Abstract

The invention discloses a comprehensive energy station-network collaborative planning method considering double uncertainties. The method of the invention considers the uncertainty of wind power and the risk preference factor of a planner, and cooperatively plans the comprehensive energy station and the energy transmission network, thereby being beneficial to improving the reliability of the system and improving the capability of the system for resisting severe scenes; by defining the size of the "risk preference parameter", the weight of the system between economy and robustness can be controlled. The method can give consideration to both economy and robustness, and by utilizing the collaborative planning of the comprehensive energy station and the energy transmission network considering double uncertainties, the planning scheme can flexibly output an economic and robust comprehensive optimal scheme.

Description

Comprehensive energy station-network collaborative planning method considering double uncertainties
Technical Field
The invention belongs to the technical field of comprehensive energy system planning, and provides a comprehensive energy station-network collaborative planning method considering double uncertainties of new energy output and risk preference.
Background
With the large-scale access of distributed renewable energy sources in an Integrated Energy System (IES), the planning of the Integrated energy System faces a series of problems such as complex models and fluctuation of output of the renewable energy sources. Comprehensive energy system planning is a common technical means for effectively solving the problems, but the scheme obtained by the existing planning method has certain limitations. Furthermore, the integrated energy station is not only an independent entity, but is usually interconnected with the energy transmission network, so that the integrated energy station will influence the planning scheme of the integrated energy system interconnected with the integrated energy station. The comprehensive energy station can obtain potential flexibility by cooperating with an energy transmission network, thereby improving the consumption of renewable energy, reducing the network investment cost and improving the asset utilization rate.
Disclosure of Invention
Aiming at the defects in the prior planning technology, the invention provides a comprehensive energy station-network collaborative planning method considering double uncertainties, which not only considers the uncertainty of the output of renewable energy, but also takes comprehensive economic optimization as a target when considering the uncertainty of decision risk preference of a planner. The method can give consideration to both economy and robustness, and by utilizing the collaborative planning of the comprehensive energy station and the energy transmission network considering double uncertainties, the planning scheme can be flexibly output to be a comprehensive optimal scheme of economy and robustness.
In order to achieve the above purpose, the invention provides a comprehensive energy station-network collaborative planning method considering double uncertainties, which comprises the following steps:
the method comprises the following steps: wind power output data of the wind turbine generator in one year are obtained, and a clustering algorithm is adopted to obtain six typical day output scenes of the wind turbine generator, namely a large wind in summer, a small wind in summer, a large wind in transition, a small wind in transition, a large wind in winter and a small wind in winter.
Step two: acquiring typical daily load curves of a comprehensive energy station in summer, a transition season and winter in a planning year and relevant parameters of comprehensive energy station-network planning, wherein the relevant parameters comprise a line/pipeline transmission upper limit, line/pipeline investment cost, equipment conversion efficiency, equipment rated capacity, equipment investment cost and equipment operation life;
step three: and establishing a comprehensive energy station-network collaborative planning model considering double uncertainties.
And selecting different nodes from the multi-node energy transmission networks with different energy types according to actual conditions to connect the comprehensive energy station. The nodes of the energy transmission network can be configured with power supplies of different energy types, and the energy conversion equipment and the energy storage equipment with different capacities are configured in the comprehensive energy station.
And adopting an interval to represent the uncertainty of the wind power output, wherein the predicted value of the wind power output is the typical daily data of the wind power generator obtained in the step one. Introducing a risk preference parameter to represent risk uncertainty of a planner, and establishing a comprehensive energy station-network collaborative planning model through an objective function and a constraint, wherein the model takes the minimum total cost as the objective function, and the total cost comprises line investment cost, pipeline investment cost, comprehensive energy station equipment investment cost and system operation cost. The constraint conditions comprise node energy balance constraint, generator/natural gas source constraint, line power/pipeline flow constraint, energy conservation constraint in a comprehensive energy station, equipment output upper and lower limit constraint and energy storage equipment constraint;
step four: and (3) decomposing the comprehensive energy station-network collaborative planning model established in the step three into a main problem and a sub problem by adopting a Column Constraint Generation (CCG) algorithm for alternative iterative solution, and calling a GUROBI solver by adopting a YALMIP optimization tool box in MATLAB to solve the comprehensive energy station-network collaborative planning model. And solving the result to be used as an equipment capacity planning result of the comprehensive energy station and an expansion planning result of the energy transmission network, and outputting the total cost under different risk preference parameters.
The target function expression of the comprehensive energy station-network collaborative planning model is as follows:
Figure BDA0003917633530000031
in the formula:
Figure BDA0003917633530000032
the annual investment cost for newly building a line is equal;
Figure BDA0003917633530000033
the investment cost of the natural gas pipeline is equal to the annual value;
Figure BDA0003917633530000034
the annual value of the investment cost of the gas compressor is equal; c eh The annual value of the investment cost of the equipment in the comprehensive energy station is equal; c gen And C gas The annual operating costs of the generator and the natural gas source, respectively.
(1) Line investment cost:
Figure BDA0003917633530000035
in the formula:
Figure BDA0003917633530000036
a variable of 0-1 which is the condition that the line l to be built is to be built or not;
Figure BDA0003917633530000037
investment cost for newly building the power transmission line for the first item; LC (liquid Crystal) e Collecting power transmission lines to be built; tau is l Representing the service life of the line l to be built; r is the discount rate; and R is the fixed asset residual value rate.
(2) Pipeline investment cost:
Figure BDA0003917633530000038
in the formula:
Figure BDA0003917633530000039
and
Figure BDA00039176335300000310
respectively representing the 0-1 variable of the first newly-built pipeline and the gas compressor to be built or not;
Figure BDA00039176335300000311
and
Figure BDA00039176335300000312
respectively expressing the investment cost of the first newly-built pipeline and the gas compressor; LC (liquid Crystal) g Is a collection of natural gas pipelines to be built.
(3) The equipment investment cost of the comprehensive energy station is as follows:
Figure BDA0003917633530000041
in the formula: n represents the total number of integrated energy stations; omega belongs to a set of CHP, CERG, HP, HS, CS and WT devices in the integrated energy station; dev represents any type of equipment in the integrated energy station; n is a radical of dev Expressed as the total capacity of dev type equipment in the integrated energy station;
Figure BDA0003917633530000042
the unit investment cost of j capacity of dev type equipment in the ith comprehensive energy station is represented;
Figure BDA0003917633530000043
a variable 0-1 which is expressed as the j capacity of the dev type equipment of the ith comprehensive energy station to be built or not;
Figure BDA0003917633530000044
an upper limit of the j-th capacity of dev-type equipment expressed as an integrated energy station; tau is dev Expressed as the operational lifetime of the dev type equipment of the integrated energy station.
(4) The operation cost is as follows:
Figure BDA0003917633530000045
in the formula: w is a s Representing a total number of scenes;
Figure BDA0003917633530000046
representing the probability of occurrence of scene s; BUS e 、BUS g Respectively representing the node sets of the generator and the natural gas source connected to the energy transmission network;
Figure BDA0003917633530000047
and
Figure BDA0003917633530000048
respectively representing the unit cost of the generator set connected to the node b and the unit cost of the natural gas source;
Figure BDA0003917633530000049
and
Figure BDA00039176335300000410
and respectively representing the actual output of the generator set connected to the node b and the natural gas source at the moment t under the scene s.
The constraint conditions comprise node energy balance constraint, generator/natural gas source constraint, line power/pipeline flow constraint, equipment output upper and lower limit constraint, energy storage equipment constraint and energy balance constraint in the comprehensive energy station. The specific constraints are as follows:
(1) Node energy balance constraint:
Figure BDA0003917633530000051
Figure BDA0003917633530000052
in the formula:
Figure BDA0003917633530000053
representing the actual output of the wind turbine generator connected to the node b at the moment t under the scene s;l1b and L2b represent sets of lines or pipes with node b as a starting point and an end point, respectively;
Figure BDA0003917633530000054
and
Figure BDA0003917633530000055
respectively representing the power of the line and the pipeline at the time t under the scene s; if the node b is connected with a comprehensive energy station
Figure BDA0003917633530000056
And
Figure BDA0003917633530000057
respectively representing the electric input and the gas input of the comprehensive energy station at the moment t under the scene s, if the node is not connected with the comprehensive energy station
Figure BDA0003917633530000058
And
Figure BDA0003917633530000059
respectively, electrical and gas loads at time t under scene s.
(2) Generator/natural gas source constraints:
Figure BDA00039176335300000510
Figure BDA00039176335300000511
Figure BDA00039176335300000512
in the formula:
Figure BDA00039176335300000513
and
Figure BDA00039176335300000514
respectively representing the lower output limit and the upper output limit of the generator set connected to the node b;
Figure BDA00039176335300000515
and
Figure BDA00039176335300000516
respectively representing the lower output limit and the upper output limit of a natural gas source connected to the node b;
Figure BDA00039176335300000517
expressed as the maximum output ramp of the genset connected at node b.
(3) Line power/pipe flow constraints:
Figure BDA00039176335300000518
Figure BDA00039176335300000519
Figure BDA00039176335300000520
Figure BDA00039176335300000521
in the formula:
Figure BDA00039176335300000522
and
Figure BDA00039176335300000523
respectively expressed as the upper limit of the transmission power of the transmission line and the upper limit of the flow of the natural gas pipeline.
(4) And (3) restraining the upper and lower limits of the output of the equipment:
Figure BDA0003917633530000061
in the formula:
Figure BDA0003917633530000062
and
Figure BDA0003917633530000063
respectively representing the power lower limit and the power upper limit of the j capacity of dev type equipment of the ith comprehensive energy station;
Figure BDA0003917633530000064
and the j capacity of the dev type equipment of the ith comprehensive energy station under the scene s is represented as the input power of t time.
(5) Energy storage equipment restraint:
Figure BDA0003917633530000065
Figure BDA0003917633530000066
Figure BDA0003917633530000067
Figure BDA0003917633530000068
Figure BDA0003917633530000069
in the formula:
Figure BDA00039176335300000610
and
Figure BDA00039176335300000611
the lower limit and the upper limit of the charging and discharging power of the energy storage device with the energy form k at the jth capacity of the ith comprehensive energy station are respectively expressed (k = h is expressed as a hot energy storage device, and k = c is expressed as a cold energy storage device);
Figure BDA00039176335300000612
and
Figure BDA00039176335300000613
respectively representing the charge and discharge state variables of the energy storage equipment with j-th capacity of the ith comprehensive energy station at the moment t in the scene s;
Figure BDA00039176335300000614
and
Figure BDA00039176335300000615
respectively representing the capacity lower limit and the capacity upper limit of the energy storage equipment under the jth capacity of the ith comprehensive energy station;
Figure BDA00039176335300000616
representing the actual capacity of the energy storage equipment with j-th capacity of the ith comprehensive energy station in a scene s at the time t;
Figure BDA00039176335300000617
and the initial capacity of the energy storage equipment with j-th capacity of the ith comprehensive energy station under the scene s is shown.
(6) Energy balance constraint in the comprehensive energy station:
Figure BDA00039176335300000618
Figure BDA0003917633530000071
Figure BDA0003917633530000072
in the formula:
Figure BDA0003917633530000073
and
Figure BDA0003917633530000074
respectively representing the total electric, cold and heat loads of the ith comprehensive energy station at the moment t under the scene s;
Figure BDA0003917633530000075
and
Figure BDA0003917633530000076
respectively representing the electricity input and the gas input from the energy transmission network at the ith comprehensive energy station in a scene s at the time t; n is a radical of chp 、N cerg 、N hp 、N hs And N cs Respectively representing the capacity sum of a cogeneration unit, a compression type refrigerating unit, an electric heat pump unit, heat energy storage equipment and cold energy storage equipment;
Figure BDA0003917633530000077
and
Figure BDA0003917633530000078
respectively representing the electric output power and the heat output power of a combined heat and power generation unit with jth capacity of the ith comprehensive energy station at the moment t under a scene s;
Figure BDA0003917633530000079
and
Figure BDA00039176335300000710
respectively representing the input power and the output power of the electric heating pump unit with the jth capacity of the ith comprehensive energy station at the moment t under the scene s;
Figure BDA00039176335300000711
and
Figure BDA00039176335300000712
respectively representing the input power and the output power of a compression type refrigerating unit with j-th capacity of the ith comprehensive energy station at the moment t under the scene s;
the comprehensive energy station-network collaborative planning model is summarized into the following form:
Figure BDA00039176335300000713
in the formula: x and y are respectively expressed as planning variables and optimization variables, and alpha and c are respectively expressed as cost parameter column vectors corresponding to x and y in the objective function; d and K are respectively expressed as coefficient matrixes of optimization variable related inequality constraint and equality constraint; f and G are respectively expressed as coefficient matrixes of inequality constraints related to planning variables and optimization variables; i is wt Expressed as an identity matrix; d. k and h are constant column vectors.
Figure BDA0003917633530000081
The actual output of the wind turbine generator can be described by an uncertain interval:
Figure BDA0003917633530000082
in the formula:
Figure BDA0003917633530000083
the predicted output curve of a typical daily scene is given in the step one;
Figure BDA0003917633530000084
and representing the maximum fluctuation deviation allowed by the output of the wind turbine.
Considering the risk preference factor of the planner, a 'risk preference parameter' gamma is introduced wt Then, the uncertain interval of the wind turbine can be converted into:
Figure BDA0003917633530000085
in the formula:
Figure BDA0003917633530000086
the variable is a binary variable, and when the value is 1, an uncertain variable representing a corresponding time period is taken to a boundary; gamma-shaped wt The wind power generation capacity is an integer with a value range of 0-24, represents the total number of time periods when the wind power output reaches the minimum value of the fluctuation interval in the dispatching cycle, and is used for meeting the requirement of a planner to adjust according to own preference.
Decomposing the comprehensive energy station-network collaborative planning model into a main problem and a sub problem through CCG, wherein the main problem is as follows:
Figure BDA0003917633530000087
in the formula: q represents the number of iterations currently performed; y is l Representing the feasible solution of the optimization variable of the main problem after the first iteration;
Figure BDA0003917633530000091
and the value of the wind turbine generator set in the main problem after the first iteration is expressed.
The sub-problems after decomposition are as follows:
Figure BDA0003917633530000092
in the formula: gamma is expressed as a dual variable of the first and second equations of the generator/natural gas source constraint, line power/piping flow constraint and the first four equations of the energy storage device constraint within the feasible region; lambda is expressed as a dual variable of a node energy balance constraint, a fifth formula of energy storage equipment and an energy conservation constraint in the comprehensive energy station in a feasible region; nu is expressed as a third formula and a fourth formula of line power/pipeline flow constraint, a fourth formula of energy storage equipment constraint and dual variables of equipment output upper and lower limit constraint in a feasible region; and pi is expressed as a dual variable of the predicted output of the wind turbine generator in a feasible region. The wind turbine output power considering the risk preference parameters is brought into a sub-problem to obtain:
Figure BDA0003917633530000093
in the formula:
Figure BDA0003917633530000094
is an introduced auxiliary variable;
Figure BDA0003917633530000095
is a sufficiently large positive real number, which is the upper bound of the dual variable pi.
The invention has the following beneficial effects:
the method of the invention considers the uncertainty of wind power and the risk preference factor of a planner, and cooperatively plans the comprehensive energy station and the energy transmission network, thereby being beneficial to improving the reliability of the system and improving the capability of the system for resisting severe scenes. By defining the size of the "risk preference parameter", the weight of the system between economy and robustness can be controlled.
Drawings
FIG. 1 is an arithmetic diagram;
FIG. 2 is a typical daily scenario output plot of wind power;
fig. 3 is a typical daily load graph.
Detailed Description
The technical scheme of the invention is further described by combining the drawings and the embodiment;
a comprehensive energy station-network collaborative planning method considering double uncertainties comprises the following steps:
the method comprises the following steps: acquiring wind power output data of the wind turbine generator in one year, and obtaining typical output scenes of six typical days including large wind in summer, small wind in summer, large wind in transition season, small wind in transition season, large wind in winter and small wind in winter by adopting a clustering algorithm.
Step two: acquiring typical daily load curves of a comprehensive energy station in summer, a transition season and winter in a planning year and relevant parameters of comprehensive energy station-network planning, wherein the relevant parameters comprise a line/pipeline transmission upper limit, line/pipeline investment cost, equipment conversion efficiency, equipment rated capacity, equipment investment cost and equipment operation life;
step three: and establishing a comprehensive energy station-network collaborative planning model considering double uncertainties.
And selecting different nodes from multi-node energy transmission networks with different energy types according to actual conditions to connect the comprehensive energy station. The nodes of the energy transmission network can be configured with power supplies of different energy types, and the energy conversion equipment and the energy storage equipment with different capacities are configured in the comprehensive energy station.
And adopting an interval to represent the uncertainty of the wind power output, wherein the predicted value of the wind power output is the typical daily data of the wind power generator obtained in the step one. Introducing a risk preference parameter to represent risk uncertainty of a planner, and establishing a comprehensive energy station-network collaborative planning model through an objective function and a constraint, wherein the model takes the minimum total cost as the objective function, and the total cost comprises line investment cost, pipeline investment cost, comprehensive energy station equipment investment cost and system operation cost. The constraint conditions comprise node energy balance constraint, generator/natural gas source constraint, line power/pipeline flow constraint, energy conservation constraint in a comprehensive energy station, equipment output upper and lower limit constraint and energy storage equipment constraint;
the target function expression of the comprehensive energy station-network collaborative planning model is as follows:
Figure BDA0003917633530000111
in the formula:
Figure BDA0003917633530000112
the investment cost for newly building a line is equal to the annual value;
Figure BDA0003917633530000113
the investment cost of the natural gas pipeline is equal to the annual value;
Figure BDA0003917633530000114
the investment cost of the gas compressor is equal to the annual value; c eh The annual value of the investment cost of the equipment in the comprehensive energy station is equal; c gen And C gas The annual operating costs of the generator and the natural gas source, respectively.
(1) Line investment cost:
Figure BDA0003917633530000115
in the formula:
Figure BDA0003917633530000116
a variable of 0-1 which is the condition that the line l to be built is to be built or not;
Figure BDA0003917633530000117
investment cost for newly building the power transmission line for the first item; LC (liquid Crystal) e The method comprises the steps of collecting power transmission lines to be built; tau. l Representing the service life of the line l to be built; r is the discount rate; and R is the fixed asset residual value rate.
(2) The investment cost of the pipeline is as follows:
Figure BDA0003917633530000118
in the formula:
Figure BDA0003917633530000119
and
Figure BDA00039176335300001110
respectively representing the 0-1 variable of the first newly-built pipeline and the gas compressor to be built or not;
Figure BDA00039176335300001111
and
Figure BDA00039176335300001112
respectively expressing the investment cost of the first newly-built pipeline and the gas compressor; LC (liquid Crystal) g Is a set of natural gas pipelines to be built.
(3) The equipment investment cost of the comprehensive energy station is as follows:
Figure BDA0003917633530000121
in the formula: n represents the total number of integrated energy stations; omega belongs to the { CHP, CERG, HP, HS, CS, WT } and is expressed as a set of equipment types in the integrated energy station; dev represents any type of equipment in the integrated energy station; n is a radical of hydrogen dev Expressed as the total capacity of dev type equipment in the integrated energy station;
Figure BDA0003917633530000122
the unit investment cost of j capacity of dev type equipment in the ith comprehensive energy station is represented;
Figure BDA0003917633530000123
a variable 0-1 which is expressed as the j capacity of the dev type equipment of the ith comprehensive energy station to be built or not;
Figure BDA0003917633530000124
an upper limit of the j-th capacity of dev-type equipment expressed as an integrated energy station; tau is dev Expressed as the operational lifetime of the dev type equipment of the integrated energy station.
(4) The operation cost is as follows:
Figure BDA0003917633530000125
in the formula: w is a s Representing a total number of scenes;
Figure BDA0003917633530000126
representing the probability of occurrence of scene s; BUS e 、BUS g Set of nodes representing generator and natural gas source connected to energy transmission network；
Figure BDA0003917633530000127
And
Figure BDA0003917633530000128
respectively representing the unit cost of the generator set connected to the node b and the unit cost of the natural gas source;
Figure BDA0003917633530000129
and
Figure BDA00039176335300001210
and respectively representing the actual output of the generator set connected to the node b and the natural gas source at the moment t under the scene s.
The constraint conditions comprise node energy balance constraint, generator/natural gas source constraint, line power/pipeline flow constraint, equipment output upper and lower limit constraint, energy storage equipment constraint and energy balance constraint in the comprehensive energy station. The specific constraints are as follows:
(1) Node energy balance constraint:
Figure BDA0003917633530000131
Figure BDA0003917633530000132
in the formula:
Figure BDA0003917633530000133
representing the actual output of the wind turbine generator connected to the node b at the moment t under the scene s; l1b and L2b represent sets of lines or pipes with node b as a start point and an end point, respectively;
Figure BDA0003917633530000134
and
Figure BDA0003917633530000135
respectively expressed as sceness power on the line and pipe at time t; if the node b is connected with a comprehensive energy station
Figure BDA0003917633530000136
And
Figure BDA0003917633530000137
respectively representing the electric input and the gas input of the comprehensive energy station at the moment t under the scene s, if the node is not connected with the comprehensive energy station
Figure BDA0003917633530000138
And
Figure BDA0003917633530000139
respectively, electrical and gas loads at time t under scene s.
(2) Generator/natural gas source constraints:
Figure BDA00039176335300001310
Figure BDA00039176335300001311
Figure BDA00039176335300001312
in the formula:
Figure BDA00039176335300001313
and
Figure BDA00039176335300001314
respectively representing the lower limit of output and the upper limit of output of the generator set connected to the node b;
Figure BDA00039176335300001315
and
Figure BDA00039176335300001316
respectively representing the lower output limit and the upper output limit of a natural gas source connected to the node b;
Figure BDA00039176335300001317
expressed as the maximum output ramp of the genset connected at node b.
(3) Line power/pipe flow constraints:
Figure BDA00039176335300001318
Figure BDA00039176335300001319
Figure BDA00039176335300001320
Figure BDA00039176335300001321
in the formula:
Figure BDA0003917633530000141
and
Figure BDA0003917633530000142
respectively expressed as the upper limit of the transmission power of the transmission line and the upper limit of the flow of the natural gas pipeline.
(4) And (3) restraining the upper and lower limits of the output of the equipment:
Figure BDA0003917633530000143
in the formula:
Figure BDA0003917633530000144
and
Figure BDA0003917633530000145
respectively representing the power lower limit and the power upper limit of the j capacity of dev type equipment of the ith comprehensive energy station;
Figure BDA0003917633530000146
and the j capacity of the dev type equipment of the ith comprehensive energy station under the scene s is represented as the input power of t time.
(5) Energy storage equipment restraint:
Figure BDA0003917633530000147
Figure BDA0003917633530000148
Figure BDA0003917633530000149
Figure BDA00039176335300001410
Figure BDA00039176335300001411
in the formula:
Figure BDA00039176335300001412
and
Figure BDA00039176335300001413
the lower limit and the upper limit of the charging and discharging power of the energy storage device with the energy form k at the jth capacity of the ith comprehensive energy station are respectively expressed (k = h is expressed as a hot energy storage device, and k = c is expressed as a cold energy storage device);
Figure BDA00039176335300001414
and
Figure BDA00039176335300001415
respectively representing the charge and discharge state variables of the energy storage equipment with j-th capacity of the ith comprehensive energy station at the moment t in the scene s;
Figure BDA00039176335300001416
and
Figure BDA00039176335300001417
respectively representing the capacity lower limit and the capacity upper limit of the energy storage equipment under the jth capacity of the ith comprehensive energy station;
Figure BDA00039176335300001418
representing the actual capacity of the energy storage equipment with j-th capacity of the ith comprehensive energy station in a scene s at the time t;
Figure BDA00039176335300001419
and the initial capacity of the energy storage equipment with j-th capacity of the ith comprehensive energy station under the scene s is shown.
(6) Energy balance constraint in the comprehensive energy station:
Figure BDA0003917633530000151
Figure BDA0003917633530000152
Figure BDA0003917633530000153
in the formula:
Figure BDA0003917633530000154
and
Figure BDA0003917633530000155
respectively representThe total amount of electric load, cold load and heat load of the ith comprehensive energy station at the moment t under a scene s;
Figure BDA0003917633530000156
and
Figure BDA0003917633530000157
respectively representing the electricity input and the gas input from the energy transmission network at the ith comprehensive energy station in a scene s at the time t; n is a radical of chp 、N cerg 、N hp 、N hs And N cs Respectively representing the capacity sum of a cogeneration unit, a compression type refrigerating unit, an electric heat pump unit, heat energy storage equipment and cold energy storage equipment;
Figure BDA0003917633530000158
and
Figure BDA0003917633530000159
respectively representing the electric output power and the heat output power of a combined heat and power generation unit with jth capacity of the ith comprehensive energy station at the moment t under a scene s;
Figure BDA00039176335300001510
and
Figure BDA00039176335300001511
respectively representing the input power and the output power of the electric heating pump unit with the jth capacity of the ith comprehensive energy station at the moment t under the scene s;
Figure BDA00039176335300001512
and
Figure BDA00039176335300001513
respectively representing the input power and the output power of a compression type refrigerating unit with j-th capacity of the ith comprehensive energy station at the moment t under the scene s;
the comprehensive energy station-network collaborative planning model is summarized into the following form:
Figure BDA00039176335300001514
in the formula: x and y are respectively expressed as planning variables and optimization variables, and alpha and c are respectively expressed as cost parameter column vectors corresponding to x and y in the objective function; d and K are respectively expressed as coefficient matrixes of optimization variable related inequality constraint and equality constraint; f and G are respectively expressed as coefficient matrixes of inequality constraints related to planning variables and optimization variables; i is wt Expressed as an identity matrix; d. k and h are constant column vectors.
Figure BDA0003917633530000161
The actual output of the wind turbine generator can be described by an uncertainty interval:
Figure BDA0003917633530000162
in the formula:
Figure BDA0003917633530000163
the predicted output curve of a typical daily scene is given in the step one;
Figure BDA0003917633530000164
and representing the maximum fluctuation deviation allowed by the output of the wind turbine.
Considering the risk preference factor of the planner, a 'risk preference parameter' gamma is introduced wt Then, the uncertain interval of the wind turbine generator can be converted into:
Figure BDA0003917633530000165
in the formula:
Figure BDA0003917633530000166
is a binary variable with a value of 1Taking an uncertain variable representing a corresponding time period to a boundary; gamma-shaped wt The wind power generation capacity is an integer with a value range of 0-24, represents the total number of time periods when the wind power output reaches the minimum value of the fluctuation interval in the dispatching cycle, and is used for meeting the requirement of a planner to adjust according to own preference.
Decomposing the comprehensive energy station-network collaborative planning model into a main problem and a sub problem through CCG, wherein the main problem is as follows:
Figure BDA0003917633530000171
in the formula: q represents the number of iterations currently performed; y is l Representing the feasible solution of the optimization variable of the main problem after the first iteration;
Figure BDA0003917633530000172
and the value of the wind turbine generator set in the main problem after the first iteration is expressed.
The sub-problems after decomposition are as follows:
Figure BDA0003917633530000173
in the formula: gamma is expressed as a dual variable of the first and second equations of the generator/natural gas source constraint, line power/piping flow constraint and the first four equations of the energy storage device constraint within the feasible region; lambda is expressed as a dual variable of a node energy balance constraint, a fifth formula of the energy storage equipment and an energy conservation constraint in the comprehensive energy station in a feasible region; v is expressed as a third formula and a fourth formula of line power/pipeline flow constraint, a fourth formula of energy storage equipment constraint and dual variables of equipment output upper and lower limit constraints in a feasible region; and pi is expressed as a dual variable of the predicted output of the wind turbine generator in a feasible region. The wind turbine output power considering the risk preference parameters is brought into a sub-problem to obtain:
Figure BDA0003917633530000174
in the formula:
Figure BDA0003917633530000175
is an introduced auxiliary variable;
Figure BDA0003917633530000176
is an upper bound on the dual variable pi and is a sufficiently large positive real number.
Step four: and (3) decomposing the comprehensive energy station-network collaborative planning model established in the step three into a main problem and a sub problem by adopting a Column Constraint Generation (CCG) algorithm for alternative iterative solution, and calling a GUROBI solver by adopting a YALMIP optimization tool box in MATLAB to solve the comprehensive energy station-network collaborative planning model. And the solution result is used as an equipment capacity planning result of the comprehensive energy station and an expansion planning result of the energy transmission network, and the total cost under different risk preference parameters is output.
Planning analysis is carried out by adopting the example shown in fig. 1, the planning result is the equipment planning result of the comprehensive energy station and the electric-gas energy network expansion planning result, and planning cost expenses under different 'risk preference parameters' are output. The power network and the natural gas network are both 6-node networks, and the nodes are connected with 5 natural gas pipelines through 7 power transmission lines, so that a topological structure of a comprehensive energy station-network is constructed. And the candidate power transmission line set and the candidate natural gas pipeline set are both existing galleries. The nodes 1, 4 and 5 are respectively connected with the comprehensive energy station 1, the comprehensive energy station 2 and the comprehensive energy station 3. The nodes 2, 3 and 6 are respectively connected with the centralized grid-connected wind turbine generator 1, the wind turbine generator 2 and the wind turbine generator 3. The nodes 3 and 6 are respectively connected with a natural gas source 1 and a natural gas source 2. The nodes 1, 2 and 6 are respectively connected with a generator set 1, a generator set 2 and a generator set 3.
FIG. 2 is a typical daily scenario output plot of wind power; fig. 3 is a typical daily load graph.
Examples
The invention establishes a comprehensive energy station-network coordination planning model considering double uncertainties, and the specific flow is as follows:
1. initializing parameters, setting a group of predicted values of wind power as an initial worst scene, setting a lower bound LB = - ∞, an upper bound UB = + ∞, and an iteration number k =1;
2. obtaining a group of predicted values of wind power according to the first step
Figure BDA0003917633530000191
Solving the main problem to obtain the optimal solution
Figure BDA0003917633530000192
With the main problem objective function value as the new lower bound
Figure BDA0003917633530000193
3. The solution obtained above
Figure BDA0003917633530000194
Substituting into the subproblem, and solving to obtain objective function value of the subproblem
Figure BDA0003917633530000195
And uncertainty variable under corresponding scenario
Figure BDA0003917633530000196
Value of
Figure BDA0003917633530000197
Updating the upper bound of a model
Figure BDA0003917633530000198
4. Given convergence accuracy as sigma, if UB-LB is less than or equal to sigma, stopping iteration and returning to optimal solution
Figure BDA0003917633530000199
And
Figure BDA00039176335300001910
otherwise increase the variable
Figure BDA00039176335300001911
And corresponding constraints. Let k = k +1, jump to convergence;
compared with the traditional robust optimization method, the comprehensive energy station-network collaborative planning model considering double uncertainties has the advantages that the economic efficiency and the robustness of a planning scheme can be flexibly considered by providing the risk preference parameters, so that the comprehensive energy station-network collaborative planning model has better engineering practical value.
Simulation result
In order to analyze the influence of the risk preference factors on the planning model, the following scenarios are set for comparative analysis. Scene S1: carrying out planning analysis by using a typical daily scene value of wind power; scene S2: on the basis of the scene 1, gamma is set wt 6, carrying out planning analysis on the model; scene S3: on the basis of scene 1, Γ is set wt At 12, a planning analysis is performed on the model; scene S4: on the basis of the scene 1, gamma is set wt At 18, a planning analysis is performed on the model; scene S5: on the basis of scene 1, Γ is set wt To 24, a planning analysis is performed on the model. The fluctuation deviation of the wind turbine generator is set to be 15% of a predicted value, 100 scenes are randomly generated according to the wind power uncertain interval, and the proportion of the planning result operable scenes in different scenes is calculated. The model planning cost results for different scenarios are shown in table 1.
TABLE 1 comparison of different scene configuration cost results
Figure BDA0003917633530000201
Firstly, as can be seen from the operable scene ratio, the scene S5 is better than S4, the scene S4 is better than S3, the scene S3 is better than S2, and the scene S2 is better than S1, it can be seen that the planner can make autonomous adjustment according to the economic and robust requirements after considering the risk preference factors. In terms of investment cost, scenes S3, S4 and S5 are the same and slightly increased compared with scenes S1 and S2; in terms of the operation cost, the operation cost gradually increases as the risk preference parameter increases.
In terms of planning and configuring the internal equipment of the integrated energy station, the configuration results of the EH1 internal equipment of all scenes are shown in table 2. Because of the influence of CS, HS configuration upper limit and CHP operation characteristics, in order to meet the requirement of large heat load, the scenes S1 to S5 are all configured with HS and EHP with the highest capacity. In contrast, the scenarios S1 and S2 increase the capacity of the CHP compared to the remaining scenarios, and reduce the capacity of the CERG with poor economy, and the planner can flexibly change and configure the capacity of the EH internal devices according to the risk preference parameters to reduce the overall cost.
TABLE 2 comparison of EH1 configuration results under different scenarios
Figure BDA0003917633530000202
In the aspect of multi-energy network planning, the configuration results of the power transmission lines in different scenes are shown in table 3, and the configuration results of the natural gas pipelines and the gas compressors in different scenes are shown in table 4.
TABLE 3 comparison of line configuration results for different scenarios
Figure BDA0003917633530000211
TABLE 4 comparison of different scene pipelines and gas compressor configuration results
Figure BDA0003917633530000212
As can be seen from tables 3 and 4, the power transmission line configuration results show that the power transmission line planning results are all construction L4 and L5 in different scenes. Comparing the scenes S2 and S1 and the scenes S4 and S3, it can be seen that the increase of the "risk preference parameter" causes the output of the wind turbine generator to decrease, and the natural gas pipeline P2 and the gas compressor Com2 are newly added. As can be derived from table 2, the CHP capacity of scenarios S3 and S4 increases. Therefore, at the cost of increasing certain pipelines and compressor configuration, the capacity of the EH internal equipment is improved, the multi-energy load requirement of the EH is met, and the economy of a planning scheme is improved.

Claims (4)

1. A comprehensive energy station-network collaborative planning method considering double uncertainties is characterized by comprising the following steps:
the method comprises the following steps: acquiring wind power output data of the wind turbine generator in one year, and obtaining typical output scenes of six typical days, namely a large wind in summer, a small wind in summer, a large wind in transition, a small wind in transition, a large wind in winter and a small wind in winter, of the wind turbine generator by adopting a clustering algorithm;
step two: acquiring typical daily load curves of a comprehensive energy station in summer, a transition season and winter in a planning year and relevant parameters of comprehensive energy station-network planning, wherein the relevant parameters comprise a line/pipeline transmission upper limit, line/pipeline investment cost, equipment conversion efficiency, equipment rated capacity, equipment investment cost and equipment operation life;
step three: establishing a comprehensive energy station-network collaborative planning model considering double uncertainties;
selecting different nodes from multi-node energy transmission networks with different energy types according to actual conditions to connect the comprehensive energy station; the nodes of the energy transmission network can be configured with power supplies of different energy types, and the energy conversion equipment and the energy storage equipment with different capacities are configured in the comprehensive energy station;
representing the uncertainty of the wind power output by adopting an interval, wherein the predicted value of the wind power output is the typical daily data of the wind power generator obtained in the step one; introducing a risk preference parameter to represent risk uncertainty of a planner, and establishing a comprehensive energy station-network collaborative planning model through a target function and constraint, wherein the model takes the minimum total cost as the target function, and the total cost comprises line investment cost, pipeline investment cost, comprehensive energy station equipment investment cost and system operation cost; the constraint conditions comprise node energy balance constraint, generator/natural gas source constraint, line power/pipeline flow constraint, energy conservation constraint in a comprehensive energy station, upper and lower limit constraint of equipment output and energy storage equipment constraint;
step four: decomposing the comprehensive energy station-network collaborative planning model established in the step three into a main problem and a sub problem by adopting a column constraint generation algorithm, and alternately and iteratively solving, and calling a GUROBI solver by adopting a YALMIP optimization tool box in MATLAB to solve the comprehensive energy station-network planning model; and solving the result to be used as an equipment capacity planning result of the comprehensive energy station and an expansion planning result of the energy transmission network, and outputting the total cost under different risk preference parameters.
2. The comprehensive energy station-network collaborative planning method considering the double uncertainties as claimed in claim 1, wherein the objective function expression of the comprehensive energy station-network collaborative planning model is as follows:
Figure FDA0003917633520000021
in the formula:
Figure FDA0003917633520000022
the investment cost for newly building a line is equal to the annual value;
Figure FDA0003917633520000023
the investment cost of the natural gas pipeline is equal to the annual value;
Figure FDA0003917633520000024
the annual value of the investment cost of the gas compressor is equal; c eh The annual value of the investment cost of the equipment in the comprehensive energy station is equal; c gen And C gas Annual operating costs for the generator and the natural gas source, respectively;
(1) Line investment cost:
Figure FDA0003917633520000025
in the formula:
Figure FDA0003917633520000026
a variable of 0-1 which is the condition that the line l to be built is to be built or not;
Figure FDA0003917633520000027
investment cost for newly building the power transmission line for the first item; LC (liquid Crystal) e The method comprises the steps of collecting power transmission lines to be built; tau is l Representing the service life of the line l to be built; r is the discount rate; r is fixed asset residual value rate;
(2) The investment cost of the pipeline is as follows:
Figure FDA0003917633520000028
in the formula:
Figure FDA0003917633520000029
and
Figure FDA00039176335200000210
respectively representing the 0-1 variable of the first newly-built pipeline and the gas compressor to be built or not;
Figure FDA0003917633520000031
and
Figure FDA0003917633520000032
respectively expressing the investment cost of the first newly-built pipeline and the gas compressor; LC (liquid Crystal) g Gathering natural gas pipelines to be built;
(3) The equipment investment cost of the comprehensive energy station is as follows:
Figure FDA0003917633520000033
in the formula: n represents the total number of integrated energy stations; omega belongs to the { CHP, CERG, HP, HS, CS, WT } and is expressed as a set of equipment types in the integrated energy station; dev represents any type of equipment in the integrated energy station; n is a radical of dev Expressed as the total capacity of dev type equipment in the integrated energy station;
Figure FDA0003917633520000034
the unit investment cost of j capacity of dev type equipment in the ith comprehensive energy station is represented;
Figure FDA0003917633520000035
a variable 0-1 which is expressed as the j capacity of the dev type equipment of the ith comprehensive energy station to be built or not;
Figure FDA0003917633520000036
an upper limit of the j-th capacity of dev-type equipment expressed as an integrated energy station; tau is dev The operational life of the dev type equipment, expressed as integrated energy stations;
(4) The operation cost is as follows:
Figure FDA0003917633520000037
in the formula: w is a s Representing a total number of scenes; p s ro Representing the probability of occurrence of scene s; BUS e 、BUS g Respectively representing a set of nodes of a generator and a natural gas source which are connected to an energy transmission network;
Figure FDA0003917633520000038
and
Figure FDA0003917633520000039
respectively representing the unit cost of the generator set connected to the node b and the unit cost of the natural gas source;
Figure FDA00039176335200000310
and
Figure FDA00039176335200000311
and respectively representing the actual output of the generator set connected to the node b and the natural gas source at the moment t under the scene s.
3. The comprehensive energy station-grid collaborative planning method considering the double uncertainties as claimed in claim 2, wherein the constraint conditions include node energy balance constraint, generator/natural gas source constraint, line power/pipeline flow constraint, equipment output upper and lower limit constraint, energy storage equipment constraint and energy balance constraint in the comprehensive energy station; the specific constraints are as follows:
(1) Node energy balance constraint:
Figure FDA0003917633520000041
Figure FDA0003917633520000042
in the formula:
Figure FDA0003917633520000043
representing the actual output of the wind turbine generator connected to the node b at the moment t under the scene s; l1b and L2b represent sets of lines or pipes with node b as a start point and an end point, respectively;
Figure FDA0003917633520000044
and
Figure FDA0003917633520000045
respectively expressed as the power on the line and the pipeline at the moment t under the scene s; if the node b is connected with a comprehensive energy station
Figure FDA0003917633520000046
And
Figure FDA0003917633520000047
respectively representing the electric input and the gas input of the comprehensive energy station at the moment t under the scene s, if the node is not connected with the comprehensive energy station
Figure FDA0003917633520000048
And
Figure FDA0003917633520000049
respectively representing the electric load and the air load at the t moment under a scene s;
(2) Generator/natural gas source constraints:
Figure FDA00039176335200000410
Figure FDA00039176335200000411
Figure FDA00039176335200000412
in the formula:
Figure FDA00039176335200000413
and
Figure FDA00039176335200000414
respectively representing the lower output limit and the upper output limit of the generator set connected to the node b;
Figure FDA00039176335200000415
and
Figure FDA00039176335200000416
respectively representing the lower output limit and the upper output limit of a natural gas source connected to the node b;
Figure FDA00039176335200000417
expressed as the maximum output climb of the generator set connected to node b;
(3) Line power/pipe flow constraints:
Figure FDA00039176335200000418
Figure FDA00039176335200000419
Figure FDA0003917633520000051
Figure FDA0003917633520000052
in the formula:
Figure FDA0003917633520000053
and
Figure FDA0003917633520000054
respectively representing the upper limit of the transmission power of the transmission line and the upper limit of the flow of the natural gas pipeline;
(4) And (3) restraining the upper and lower limits of the output of the equipment:
Figure FDA0003917633520000055
in the formula:
Figure FDA0003917633520000056
and
Figure FDA0003917633520000057
respectively representing the power lower limit and the power upper limit of the j capacity of dev type equipment of the ith comprehensive energy station;
Figure FDA0003917633520000058
the input power of the j capacity of the dev type equipment of the ith comprehensive energy station at the time t under the scene s is represented;
(5) Energy storage equipment restraint:
Figure FDA0003917633520000059
Figure FDA00039176335200000510
Figure FDA00039176335200000511
Figure FDA00039176335200000512
Figure FDA00039176335200000513
in the formula:
Figure FDA00039176335200000514
and
Figure FDA00039176335200000515
respectively representing the lower limit and the upper limit of the charging and discharging power of the energy storage device with the energy form of k under the j capacity of the ith comprehensive energy station, wherein k = h represents a hot energy storage device, and k = c represents a cold energy storage device;
Figure FDA00039176335200000516
and
Figure FDA00039176335200000517
respectively expressed as under scene sThe charging and discharging state variable of the energy storage equipment with the jth capacity of the ith comprehensive energy station at the time t;
Figure FDA00039176335200000518
and
Figure FDA00039176335200000519
respectively representing the capacity lower limit and the capacity upper limit of the energy storage equipment under the jth capacity of the ith comprehensive energy station;
Figure FDA00039176335200000520
representing the actual capacity of the energy storage equipment with j-th capacity of the ith comprehensive energy station in a scene s at the time t;
Figure FDA00039176335200000521
the initial capacity of the energy storage equipment is expressed as the jth capacity of the ith comprehensive energy station under the scene s;
(6) Energy balance constraint in the comprehensive energy station:
Figure FDA0003917633520000061
Figure FDA0003917633520000062
Figure FDA0003917633520000063
in the formula:
Figure FDA0003917633520000064
and
Figure FDA0003917633520000065
respectively expressed as the electricity of the ith comprehensive energy station at the moment t under the scene sTotal cold and heat loads;
Figure FDA0003917633520000066
and
Figure FDA0003917633520000067
respectively representing the electricity input and the gas input from the energy transmission network at the ith comprehensive energy station in a scene s at the time t; n is a radical of chp 、N cerg 、N hp 、N hs And N cs Respectively representing the capacity sum of a cogeneration unit, a compression type refrigerating unit, an electric heat pump unit, heat energy storage equipment and cold energy storage equipment;
Figure FDA0003917633520000068
and
Figure FDA0003917633520000069
respectively representing the electric output power and the heat output power of a combined heat and power generation unit with j-th capacity of the ith comprehensive energy station at the moment t under the scene s;
Figure FDA00039176335200000610
and
Figure FDA00039176335200000611
respectively representing the input power and the output power of the electric heating pump unit with the jth capacity of the ith comprehensive energy station at the moment t under the scene s;
Figure FDA00039176335200000612
and
Figure FDA00039176335200000613
and respectively representing the input power and the output power of the compression type refrigerating unit with the jth capacity of the ith comprehensive energy station in the scene s at the moment t.
4. The comprehensive energy station-network collaborative planning method considering the double uncertainty as claimed in claim 3, wherein the comprehensive energy station-network collaborative planning model is summarized in the form of:
Figure FDA0003917633520000071
in the formula: x and y are respectively expressed as planning variables and optimization variables, and alpha and c are respectively expressed as cost parameter column vectors corresponding to x and y in the objective function; d and K are respectively expressed as coefficient matrixes of optimization variable related inequality constraint and equality constraint; f and G are respectively expressed as coefficient matrixes of inequality constraints related to planning variables and optimization variables; i is wt Expressed as an identity matrix; d. k and h are constant column vectors;
Figure FDA0003917633520000072
the actual output of the wind turbine generator can be described by an uncertain interval:
Figure FDA0003917633520000073
in the formula:
Figure FDA0003917633520000074
the predicted output curve of a typical daily scene is given in the step one;
Figure FDA0003917633520000075
representing the maximum fluctuation deviation allowed by the output of the wind turbine;
considering the risk preference factor of the planner, a 'risk preference parameter' gamma is introduced wt Then, the uncertain interval of the wind turbine can be converted into:
Figure FDA0003917633520000076
in the formula:
Figure FDA0003917633520000077
the variable is a binary variable, and when the value is 1, an uncertain variable representing a corresponding time period is taken to a boundary; gamma-shaped wt The wind power generation capacity is an integer with a value range of 0-24, represents the total number of time periods when the wind power output reaches the minimum value of the fluctuation interval in the scheduling period, and is used for meeting the requirement of a planner to adjust according to own preference;
decomposing the comprehensive energy station-network collaborative planning model into a main problem and a sub problem through CCG, wherein the main problem is as follows:
Figure FDA0003917633520000081
in the formula: q represents the number of iterations currently performed; y is l Representing the feasible solution of the optimization variable of the main problem after the first iteration;
Figure FDA0003917633520000082
the value of the wind turbine generator set in the main problem after the first iteration is expressed;
the sub-problems after decomposition are as follows:
Figure FDA0003917633520000083
in the formula: gamma is expressed as a dual variable of the first and second equations of the generator/natural gas source constraint, line power/piping flow constraint and the first four equations of the energy storage device constraint within the feasible region; lambda is expressed as a dual variable of a node energy balance constraint, a fifth formula of the energy storage equipment and an energy conservation constraint in the comprehensive energy station in a feasible region; v is expressed as a third formula and a fourth formula of line power/pipeline flow constraint, a fourth formula of energy storage equipment constraint and dual variables of equipment output upper and lower limit constraints in a feasible region; pi is expressed as a dual variable of the predicted output of the wind turbine generator in a feasible region; the wind turbine output power considering the risk preference parameters is brought into a sub-problem to obtain:
Figure FDA0003917633520000091
in the formula:
Figure FDA0003917633520000092
is an introduced auxiliary variable;
Figure FDA0003917633520000093
is an upper bound on the dual variable pi and is a sufficiently large positive real number.
CN202211347153.4A 2022-10-31 2022-10-31 Comprehensive energy station-network collaborative planning method considering double uncertainties Pending CN115545340A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116205080A (en) * 2023-03-23 2023-06-02 盛东如东海上风力发电有限责任公司 Method and system for determining efficiency curve of each part of wind turbine generator

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116205080A (en) * 2023-03-23 2023-06-02 盛东如东海上风力发电有限责任公司 Method and system for determining efficiency curve of each part of wind turbine generator
CN116205080B (en) * 2023-03-23 2024-05-31 盛东如东海上风力发电有限责任公司 Method and system for determining efficiency curve of each part of wind turbine generator

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