CN113364051B - Multi-power-supply system capacity configuration scheduling method and device considering offshore wind power access - Google Patents

Multi-power-supply system capacity configuration scheduling method and device considering offshore wind power access Download PDF

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CN113364051B
CN113364051B CN202110692975.5A CN202110692975A CN113364051B CN 113364051 B CN113364051 B CN 113364051B CN 202110692975 A CN202110692975 A CN 202110692975A CN 113364051 B CN113364051 B CN 113364051B
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offshore wind
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袁振华
鉴庆之
李文升
王亮
刘晓明
田鑫
杨思
张辉
程佩芬
王男
张丽娜
孙东磊
牟颖
杜欣烨
孙永辉
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State Grid Corp of China SGCC
Hohai University HHU
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Hohai University HHU
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a multi-power-supply system capacity allocation scheduling method and device considering offshore wind power access, which comprises the steps of firstly selecting power supply and energy storage types according to output characteristics and complementary relation, and establishing a multi-power-supply system model considering offshore wind power cluster access; then establishing a multi-power-supply system construction, operation and maintenance cost model according to the selected power supply and the stored energy; secondly, constructing a multi-power-supply system double-layer optimization configuration planning model by taking the optimal total cost as a target function, and simultaneously taking the prediction error of the offshore wind power into consideration to perform robust optimization on the model; and finally, calling a self-adaptive inertial weight particle swarm algorithm and a Cplex solver to solve the upper layer model and the lower layer model to obtain a capacity configuration and scheduling planning scheme of the multi-power-supply system considering offshore wind power access. According to the method, based on the robust optimization and double-layer optimization capacity configuration and scheduling planning method, a multi-power configuration planning scheme aiming at offshore wind power access and considering prediction errors is obtained, and the economical efficiency and the reliability of the operation of the power system are improved.

Description

Capacity allocation scheduling method and device of multi-power-supply system considering offshore wind power access
Technical Field
The invention belongs to the field of comprehensive energy system planning, and particularly relates to a capacity allocation scheduling method and device of a multi-power-supply system considering offshore wind power access.
Background
The current growing energy and environmental problems have created a global large trend towards the large scale development of non-fossil energy sources. The basic trend of global energy transformation is to realize the transformation from a fossil energy system to a low-carbon energy system, and the ultimate goal is to enter a sustainable energy era mainly based on renewable energy. From the development of current global offshore wind power, a plurality of offshore wind power plants with different capacities, adjacent positions and different ownership are generally planned and constructed in the same sea area, and the offshore wind power development shows obvious clustering and scale characteristics. The offshore wind power is influenced by natural factors (including climate, temperature and the like), the output of the offshore wind power has uncertain characteristics such as randomness, intermittence and the like, and the large-scale offshore wind power is accessed, so that the peak regulation pressure of a power grid, the pressure supported by the voltage of a receiving end power grid, the difficulty of optimal configuration of active and reactive spare capacity and the like are further increased except for the increase of uncertainty of state information of the power grid.
In recent years, the development speed of offshore wind power is increased continuously, and the large-scale offshore wind power access receiving end power grid analysis and grid-connected planning become research hotspots. The existing offshore wind power plants are mostly offshore wind power plants, intertidal zones and beach wind power plants, the grid-connected mode mostly adopts an alternating current grid-connected mode or a double-end direct current grid-connected mode, the capacity of the wind power plants is smaller relative to a receiving-end power grid, the influence on the receiving-end power grid is smaller, and planning design consideration factors are fewer. At present, the research on multi-power supply optimal configuration of a power system under wind power integration is abundant, but a great deal of blank still exists in collaborative planning strategy research aiming at large-scale offshore wind power and a receiving-end power grid, and with the development of the large-scale offshore wind power, the existing planning and designing technology cannot be directly applied to the aspect of large-scale offshore wind power integration planning research. The large-capacity offshore wind power is merged into a receiving-end power grid, the interaction influence of the large-capacity offshore wind power and the receiving-end power grid is more complex, more factors need to be considered in planning and design, and the difficulty is higher. Therefore, aiming at the problem of optimal configuration of a plurality of types of power supplies of a receiving-end power grid suitable for large-scale offshore wind power access, a more scientific and effective planning and research tool and method need to be established urgently.
Disclosure of Invention
The invention aims to: the invention aims to provide a method and a device for configuring and scheduling capacity of a multi-power-supply system in consideration of offshore wind power access, and provides support for power capacity optimal configuration and operation scheduling planning of the multi-power-supply system in offshore wind power cluster access.
The invention content is as follows: the invention provides a capacity configuration and scheduling planning method of a multi-power-supply system considering offshore wind power access, which specifically comprises the following steps:
(1) Selecting power supply and energy storage types according to the output characteristics and the complementary relationship, and establishing a multi-power-supply system model considering the access of the offshore wind power cluster;
(2) Establishing a multi-power-supply system construction, operation and maintenance cost model according to the selected power supply and the stored energy;
(3) Considering the operation constraint conditions of the generator set in the multi-power system;
(4) Constructing a double-layer optimization configuration planning model of the multi-power system by taking the optimal total cost as a target function, and carrying out robust optimization on a lower-layer model by considering the prediction error of offshore wind power;
(5) Calling a self-adaptive inertia weight particle swarm algorithm and a Cplex solver to solve an upper layer optimization model and a lower layer optimization model;
(6) And outputting a power supply capacity configuration scheme with optimal cost and an operation planning result.
Further, the multi-power-supply system model in the step (1) is a multi-power-supply system comprising thermal power generation, gas turbine power generation and pumped storage.
Further, the building of the multi-power-supply system building and maintenance cost model in the step (2) is realized by allocating the model into each scheduling period in the service life through the discount rate, and the implementation process is as follows:
Figure BDA0003126888670000021
wherein:
Figure BDA0003126888670000022
wherein the content of the first and second substances,
Figure BDA0003126888670000023
is a system
Figure BDA0003126888670000024
The unit cost of manufacture;
Figure BDA0003126888670000025
is a system
Figure BDA0003126888670000026
The capacity of (c);
Figure BDA0003126888670000027
is a system
Figure BDA0003126888670000028
The service life of the internal unit; lambda is the discount rate;
Figure BDA0003126888670000029
is a system
Figure BDA00031268886700000210
The ratio of maintenance cost to construction cost.
Further, the step (2) of establishing the multi-power-supply system operation cost model is to respectively establish a system internal unit fuel consumption cost model and a starting cost model, and the multi-power-supply system operation cost is as follows:
C OP (t)＝C FO (t)+C TO (t)+C PO (t)
in the formula, C FO 、C TO The fuel consumption costs of a thermal power system and a gas turbine power generation system in the period t are respectively; c PO The starting cost of the pumped storage system is t time period;
the system burn-up cost model is:
Figure BDA0003126888670000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000032
respectively outputting power of the thermal power generating unit i and the gas turbine unit g in a time period t; a is a i 、b i 、c i The correlation coefficient of the operation cost of the thermal power generating unit i is obtained; p is a radical of n Is the unit burn-up cost of natural gas; eta is the generating efficiency of the gas turbine set; n is a radical of G 、N T Respectively the total number of the thermal power generating unit and the gas turbine unit;
the system startup cost model is:
Figure BDA0003126888670000033
Figure BDA0003126888670000034
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000035
starting costs of the pumped storage unit k during power generation and water pumping in the time period t are respectively set; s is gen,k 、s pum,k Starting costs of power generation and water pumping of the water pumping and energy storage unit k are respectively saved;
Figure BDA0003126888670000036
representing that the pumped storage unit k is in a power generation state in a time period t;
Figure BDA0003126888670000037
indicating that the pumped storage unit k is in a pumped state in the time period t; n is a radical of H Is the total number of the pumping unit.
Further, the generator set operation constraint conditions in the multi-power-supply system in the step (3) are as follows:
Figure BDA0003126888670000038
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000039
the power generation and pumping power of the pumped storage unit k in the time period t are respectively;
Figure BDA00031268886700000310
the predicted value is the wind power value of the offshore wind farm w in the t time period;
Figure BDA00031268886700000311
the abandoned wind power of the offshore wind farm w in the period t;
Figure BDA00031268886700000312
the active power predicted value of the load node d in the t period; n is a radical of hydrogen W The total number of the offshore wind turbine generator sets; d is the number of load nodes;
the constraint of the abandoned wind power of the offshore wind power is as follows:
Figure BDA00031268886700000313
in the formula, e is the upper limit of the wind curtailment rate of the offshore wind power;
the thermal power generating unit operation constraint conditions comprise:
Figure BDA00031268886700000314
Figure BDA00031268886700000315
in the formula (I), the compound is shown in the specification,
Figure BDA00031268886700000316
for the starting and stopping conditions of the thermal power generating unit i in the time period t,
Figure BDA00031268886700000317
indicating that the thermal power generating unit i is in a shutdown state during the period t,
Figure BDA0003126888670000041
indicating that the thermal power generating unit i is in an operating state in a time period t;
Figure BDA0003126888670000042
respectively obtaining the minimum and maximum output power allowed by the thermal power generating unit i;
Figure BDA0003126888670000043
respectively the maximum value of the thermal power generating unit in unit timeLoad shedding, load rate limits; Δ t is a scheduling time interval;
the gas turbine unit operation constraint conditions include:
Figure BDA0003126888670000044
Figure BDA0003126888670000045
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000046
is the starting and stopping condition of the gas turbine set g in the time period t,
Figure BDA0003126888670000047
indicating that the gas turbine group g is in a stopped state for a period t,
Figure BDA0003126888670000048
indicating that the gas turbine unit g is in an operating state in a time period t;
Figure BDA0003126888670000049
respectively the minimum and maximum output power allowed by the gas turbine unit g;
Figure BDA00031268886700000410
maximum load shedding and loading rate limit values of the gas turbine unit g in unit time are respectively set;
the pumped-storage unit operation constraints may be:
Figure BDA00031268886700000411
in the formula (I), the compound is shown in the specification,
Figure BDA00031268886700000412
whether the pumped storage unit k is in a power generation state and a water pumping state in a time period t respectively, wherein 1 represents that the unit k is in a corresponding stateState, 0 means not in the corresponding state;
Figure BDA00031268886700000413
respectively the minimum and maximum generating power of the pumped storage group k within the allowable range
Figure BDA00031268886700000414
Respectively the minimum pumping power and the maximum pumping power within the allowable range of the pumped storage unit k;
the reservoir capacity constraint of the pumped storage power station is as follows:
Figure BDA00031268886700000415
in the formula, W t The water quantity of the upper reservoir is t time period;
Figure BDA00031268886700000416
average water quantity/electric quantity conversion coefficients of water pumping and power generation states in a time period t are respectively set; w min 、W max The minimum water quantity and the maximum water quantity of the upper reservoir are respectively.
Further, the objective function in step (4) is:
Figure BDA00031268886700000417
in the formula, C OP (t) cost of operating the multiple power supply system, C IM The construction and maintenance cost of a multi-power system is reduced.
Further, the building of the double-layer optimized configuration planning model of the multi-power-supply system in the step (4) is that the upper-layer optimized model transmits the multi-type power supply capacity configuration schemes to the lower-layer model, the lower-layer model generates an operation scheduling plan with the optimal cost according to the power supply capacity, and the minimum cost result is returned to the upper-layer model; and the upper layer model optimizes the capacity configuration according to the return result.
Further, in step (4), considering the prediction error of the offshore wind power, the robust optimization process for the lower layer model is as follows:
Figure BDA0003126888670000051
wherein x is the traditional power output; epsilon is a predicted value of the offshore wind power, belongs to an uncertain set U, and U is a bounded set; f is an objective function; g is a constraint function.
Based on the same inventive concept, the invention also provides a multi-power-supply system capacity configuration scheduling device considering offshore wind power access, which comprises a memory, a processor and a computer program, wherein the computer program is stored on the memory and can run on the processor; the computer program, when loaded into the processor, implements the above-described method for scheduling a configuration of capacity of a multi-power system taking into account offshore wind power access.
Has the advantages that: compared with the prior art, the invention has the following beneficial effects: 1. the coordination optimization strategy aiming at the combined output of the offshore wind power and the traditional power supply gives consideration to the system operation economy and the offshore wind power consumption level, and improves the flexibility of system operation scheduling; the double-layer optimized configuration planning model of the multi-power system enhances the containment of offshore abnormal wind conditions in the power planning process through robust optimization, and realizes the stable and reliable operation of the system; 2. the model and the method provide theoretical guidance for power capacity configuration and operation scheduling planning of a multi-source system accessed by offshore wind power, and provide necessary technical support for a power planning technology considering offshore wind power integration.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a scheduling plan diagram of a multi-power system according to the present embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention provides a capacity allocation scheduling method of a multi-power-supply system considering offshore wind power access, which comprises the steps of determining power supplies and energy storage models in the multi-power-supply system by considering offshore wind power access as shown in figure 1; initializing, setting particle swarm parameters, and generating a multi-type power supply capacity initialization population; substituting the upper-layer decision quantity into the lower-layer robust optimization model to obtain operation scheduling planning and operation cost; and finding out the current optimal position of each particle and the optimal position of the whole particle swarm. And updating the speed and the position of each particle, generating a new population, circulating the steps until a termination condition is met, and outputting a capacity allocation and scheduling planning scheme with optimal cost. The method specifically comprises the following steps:
step 1: and selecting power supply and energy storage types according to the output characteristics and the complementary relation, and establishing a multi-power-supply system model considering the access of the offshore wind power cluster.
Complementary characteristics and synergistic effects of different energy forms are analyzed, a multi-source system is formed by considering thermal power generation, gas turbine power generation and pumped storage together aiming at an electric power system accessed by a large-scale offshore wind power cluster, and unit parameters are selected.
Step 2: and establishing a multi-power-supply system construction, operation and maintenance cost model according to the selected power supply and the stored energy.
And (3) the over-discount rate distributes the system construction and maintenance cost into each scheduling period in the service life, and a multi-power-supply system construction, operation and maintenance cost model is established by considering the unit fuel consumption cost and the starting cost.
The construction and maintenance cost of the multi-power system is as follows:
Figure BDA0003126888670000061
wherein:
Figure BDA0003126888670000062
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000065
is a system
Figure BDA0003126888670000066
The unit cost of manufacture;
Figure BDA00031268886700000611
is a system
Figure BDA00031268886700000612
The capacity of (a);
Figure BDA00031268886700000610
is a system
Figure BDA0003126888670000069
The service life of the internal unit; lambda is the discount rate;
Figure BDA0003126888670000067
is a system
Figure BDA0003126888670000068
The ratio of maintenance cost to construction cost.
The operation cost of the multi-power system is as follows:
C OP (t)＝C FO (t)+C TO (t)+C PO (t)
in the formula, C FO 、C TO The fuel consumption costs of a thermal power system and a gas turbine power generation system in the period t are respectively; c PO And starting cost of the pumped storage system for the period t.
The burn-up cost model for the system can be expressed as:
Figure BDA0003126888670000063
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000064
respectively outputting power of the thermal power generating unit i and the gas turbine unit g in a time period t; a is i 、b i 、c i The correlation coefficient of the operation cost of the thermal power generating unit i is obtained; p is a radical of formula n Is the unit burn-up cost of natural gas; eta is the generating efficiency of the gas turbine set; n is a radical of G 、N T Respectively the total number of the thermal power generating unit and the gas turbine unit.
The cost model for system startup can be expressed as:
Figure BDA0003126888670000071
Figure BDA0003126888670000072
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000073
starting costs of the pumped storage unit k during power generation and water pumping in the time period t are respectively set; s is gen,k 、s pum,k Starting costs of power generation and water pumping of the water pumping and energy storage unit k are respectively saved;
Figure BDA0003126888670000074
representing that the pumped storage unit k is in a power generation state in a time period t;
Figure BDA0003126888670000075
indicating that the pumped storage unit k is in a pumped state in the time period t; n is a radical of hydrogen H Is the total number of the pumping unit.
And 3, step 3: and considering the operation constraint conditions of the generator set in the multi-power system.
And (3) power balance constraint in a multi-power system:
Figure BDA0003126888670000076
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000077
respectively outputting power of the thermal power generating unit i and the gas turbine unit g in a time period t;
Figure BDA0003126888670000078
t the power generation and pumping power of the pumped storage unit k in the time period t are respectively;
Figure BDA0003126888670000079
the wind power prediction value of the offshore wind farm w at the t period is obtained;
Figure BDA00031268886700000710
the abandoned wind power of the offshore wind farm w in the period t;
Figure BDA00031268886700000711
the active power predicted value of the load node d in the t period is obtained; n is a radical of G 、N T 、N H 、N W The total number of the thermal power generating unit, the gas turbine unit, the pumping and storage unit and the offshore wind power generating unit is respectively; and D is the number of the load nodes.
And (3) limiting the abandoned wind power of the offshore wind power:
Figure BDA00031268886700000712
in the formula, e is the upper limit of the wind curtailment rate of the offshore wind power.
And (3) operation constraint of the thermal power generating unit:
Figure BDA00031268886700000713
Figure BDA00031268886700000714
in the formula (I), the compound is shown in the specification,
Figure BDA00031268886700000715
for the starting and stopping conditions of the thermal power generating unit i in the time period t,
Figure BDA00031268886700000716
indicating that the thermal power generating unit i is in a shutdown state during the period t,
Figure BDA00031268886700000717
indicating that the thermal power generating unit i is in the t periodAn operating state;
Figure BDA00031268886700000718
respectively obtaining the minimum and maximum output power allowed by the thermal power generating unit i;
Figure BDA00031268886700000719
respectively setting maximum load shedding and loading rate limit values of the thermal power generating unit i in unit time; Δ t is the scheduling time interval.
Gas turbine unit operation constraint:
Figure BDA00031268886700000720
Figure BDA0003126888670000081
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000082
is the starting and stopping condition of the gas turbine set g in the time period t,
Figure BDA0003126888670000083
indicating that the gas turbine group g is in a stopped state for a period t,
Figure BDA0003126888670000084
indicating that the gas turbine unit g is in an operating state in a time period t;
Figure BDA0003126888670000085
respectively the minimum and maximum output power allowed by the gas turbine unit g;
Figure BDA0003126888670000086
the maximum load shedding and loading rate limit values of the gas turbine unit g in unit time are respectively.
And (3) operation constraint of the pumped storage unit:
Figure BDA0003126888670000087
in the formula (I), the compound is shown in the specification,
Figure BDA0003126888670000088
whether the pumped storage unit k is in a power generation state and a pumping state in a time period t is respectively determined, wherein 1 represents that the pumped storage unit k is in a corresponding state, and 0 represents that the pumped storage unit k is not in a corresponding state;
Figure BDA0003126888670000089
respectively the minimum power generation power and the maximum power generation power within the allowable range of the pumped storage group k;
Figure BDA00031268886700000810
respectively the minimum pumping power and the maximum pumping power within the allowable range of the pumped storage group k.
And (3) reservoir capacity constraint of the pumped storage power station:
Figure BDA00031268886700000811
in the formula, W t The water quantity of the upper reservoir is t time period;
Figure BDA00031268886700000812
average water quantity/electric quantity conversion coefficients of water pumping and power generation states in a time period t are respectively set; w is a group of min 、W max The minimum and maximum water quantities of the upper reservoir are respectively.
And 4, step 4: and constructing a double-layer optimization configuration planning model of the multi-power-supply system by taking the optimal total cost as a target function, and carrying out robust optimization on the lower-layer model by considering the prediction error of the offshore wind power.
The minimum total system cost including construction and operation and maintenance costs is taken as an objective function, and the mathematical model is as follows:
Figure BDA00031268886700000813
wherein, C OP (t) cost of operating the multiple power supply system, C IM Cost of construction and maintenance for multiple power systems
The upper-layer optimization model transmits the multi-type power supply capacity configuration scheme to the lower-layer model, the lower-layer model generates an operation scheduling plan with optimal cost according to the power supply capacity, and a minimum cost result is returned to the upper-layer model; then, the upper layer model optimizes the capacity configuration according to the returned result.
And (4) considering the prediction error of the offshore wind power, and performing robust optimization on the lower layer model.
The optimization problem with uncertain parameters can be described as follows:
Figure BDA0003126888670000091
wherein x is a decision variable; epsilon is an uncertain quantity and belongs to an uncertain set U; f is an objective function; g is a constraint function. If U is a bounded set, the above equation is called a robust optimization problem.
And 5: and calling an adaptive inertia weight particle swarm algorithm and a Cplex solver to solve the upper and lower layer optimization models. And outputting a power supply capacity configuration scheme with optimal cost and an operation planning result.
And calling an adaptive inertia weight particle swarm algorithm and a Cplex solver to solve the double-layer optimization model. In the basic particle swarm algorithm, the inertia weight omega is a fixed value, when omega is smaller, the local space searching capability of the algorithm can be improved, but the capability of searching a new area is weaker, and the convergence speed is low; when the inertia factor ω is large, the global space search capability of the algorithm is improved, but the local search capability is weak, and convergence may not be achieved. Aiming at the advantages and disadvantages of the basic particle swarm inertial factor omega, the adaptive weight particle swarm optimization algorithm dynamically adjusts the inertial weight omega according to the premature convergence degree and the fitness value
Based on the same inventive concept, the invention also provides a multi-power-supply system capacity configuration scheduling device considering offshore wind power access, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor; the computer program, when loaded into the processor, implements the above-described method for scheduling a configuration of capacity of a multi-power system taking into account offshore wind power access.
To verify the effectiveness of the method of the invention, the following experiments were performed; and performing power supply capacity configuration and operation scheduling planning of the multi-source receiving-end power grid by using typical daily power prediction data of a certain offshore wind farm in Shandong province in China. The predicted maximum value of offshore wind power is 3500MW, the minimum value is 794MW, and the error is set to +/-10%. Four thermal power plants, four gas turbine power plants and two pumped storage power plants in the multi-power supply system are used as supplementary power supplies to achieve power balance and cost optimization in each time period. By taking the prediction error into account, the power capacity configuration result is shown in table 1 below, and the operation scheduling planning result is shown in fig. 2 below.
Table 1 power supply capacity configuration results
Figure BDA0003126888670000092
Figure BDA0003126888670000101
According to the optimization result, the offshore wind power curtailment is controlled within 20%, the total construction cost in a scheduling period is 129.081 ten thousand yuan, and the total operation and maintenance cost is 11454.245 ten thousand yuan. In conclusion, the power capacity configuration and scheduling planning scheme of the multi-power-supply system considering offshore wind power access and considering reliability and economy can be obtained, and the method can be applied to practical engineering application.

Claims (4)

1. A capacity configuration scheduling method of a multi-power-supply system considering offshore wind power access is characterized by comprising the following steps:
(1) Selecting power supply and energy storage types according to the output characteristics and the complementary relation, and establishing a multi-power-supply system model considering the access of the offshore wind power cluster;
(2) Establishing a multi-power-supply system construction, operation and maintenance cost model according to the selected power supply and the stored energy;
(3) Considering the operation constraint conditions of generator sets in a multi-power system;
(4) Constructing a multi-power-supply-system double-layer optimization configuration planning model by taking the optimal total cost as a target function, and carrying out robust optimization on a lower-layer model by considering the prediction error of the offshore wind power;
(5) Calling a self-adaptive inertia weight particle swarm algorithm and a Cplex solver to solve the upper and lower layer optimization models;
(6) Outputting a power supply capacity configuration scheme with optimal cost and an operation planning result;
the step (2) of establishing the multi-power system operation cost model is to respectively establish a unit fuel consumption cost model and a starting cost model in the system, and the multi-power system operation cost is as follows:
C OP (t)＝C FO (t)+C TO (t)+C PO (t)
in the formula, C FO 、C TO The fuel consumption costs of a thermal power system and a gas turbine power generation system in the period t are respectively; c PO The starting cost of the pumped storage system is t time period;
the system burn-up cost model is:
Figure FDA0003822593480000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003822593480000012
respectively outputting power of the thermal power generating unit i and the gas turbine unit g in a time period t; a is i 、b i 、c i The correlation coefficient of the operation cost of the thermal power generating unit i is obtained; p is a radical of n For unit consumption of natural gasThen, the process is carried out; eta is the generating efficiency of the gas turbine set; n is a radical of G 、N T Respectively the total number of the thermal power generating unit and the gas turbine unit;
the system startup cost model is:
Figure FDA0003822593480000013
Figure FDA0003822593480000014
in the formula (I), the compound is shown in the specification,
Figure FDA0003822593480000015
respectively representing the starting cost of the pumped storage unit k during power generation and pumping in the time period t; s gen,k 、s pum,k Starting costs of power generation and water pumping of the water pumping and energy storage unit k are respectively set;
Figure FDA0003822593480000021
representing that the pumped storage unit k is in a power generation state in a time period t;
Figure FDA0003822593480000022
indicating that the pumped storage unit k is in a pumped state in the time period t; n is a radical of H The total number of the pumping and storage units;
and (3) the generator set operation constraint conditions in the multi-power system are as follows:
Figure FDA0003822593480000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003822593480000024
the power generation and pumping power of the pumped storage unit k in the time period t are respectively;
Figure FDA0003822593480000025
the wind power prediction value of the offshore wind farm w at the t period is obtained;
Figure FDA0003822593480000026
the abandoned wind power of the offshore wind farm w in the period t;
Figure FDA0003822593480000027
the active power predicted value of the load node d in the t period is obtained; n is a radical of W The total number of the offshore wind turbine generators is; d is the number of load nodes;
the constraint of the abandoned wind power of the offshore wind power is as follows:
Figure FDA0003822593480000028
in the formula, e is the upper limit of the wind abandoning rate of the offshore wind power;
the thermal power generating unit operation constraint conditions comprise:
Figure FDA0003822593480000029
Figure FDA00038225934800000210
in the formula (I), the compound is shown in the specification,
Figure FDA00038225934800000211
for the starting and stopping conditions of the thermal power generating unit i in the time period t,
Figure FDA00038225934800000212
indicating that the thermal power generating unit i is in a shutdown state during the period t,
Figure FDA00038225934800000213
indicating that the thermal power generating unit i is in an operating state in a period t;
Figure FDA00038225934800000214
respectively obtaining the minimum and maximum output power allowed by the thermal power generating unit i;
Figure FDA00038225934800000215
respectively setting maximum load shedding and loading rate limit values of the thermal power generating unit i in unit time; Δ t is a scheduling time interval;
the gas turbine unit operation constraint conditions include:
Figure FDA00038225934800000216
Figure FDA00038225934800000217
in the formula (I), the compound is shown in the specification,
Figure FDA00038225934800000218
is the starting and stopping condition of the gas turbine set g in the time period t,
Figure FDA00038225934800000219
indicating that the gas turbine group g is in a stopped state for a period of time t,
Figure FDA00038225934800000220
indicating that the gas turbine unit g is in an operating state in a time period t;
Figure FDA00038225934800000221
respectively the minimum and maximum output power allowed by the gas turbine unit g;
Figure FDA00038225934800000222
maximum load shedding and loading rate limit values of the gas turbine unit g in unit time are respectively set;
the pumped-storage unit operation constraints may be:
Figure FDA0003822593480000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003822593480000032
whether the pumped storage unit k is in a power generation state and a pumping state in a time period t is respectively determined, wherein 1 represents that the pumped storage unit k is in a corresponding state, and 0 represents that the pumped storage unit k is not in a corresponding state;
Figure FDA0003822593480000033
respectively the minimum and maximum generating power of the pumped storage group k within the allowable range;
Figure FDA0003822593480000034
respectively the minimum pumping power and the maximum pumping power within the allowable range of the pumped storage unit k;
the constraint of the storage capacity of the pumped storage power station is as follows:
Figure FDA0003822593480000035
in the formula, W t The water quantity of the upper reservoir is t time period;
Figure FDA0003822593480000036
average water quantity/electric quantity conversion coefficients of water pumping and power generation states in a time period t are respectively set; w min 、W max The minimum water quantity and the maximum water quantity of the upper reservoir are respectively;
the objective function in the step (4) is as follows:
Figure FDA0003822593480000037
in the formula, C OP (t) cost of operating the multiple power supply system, C IM For building and maintaining multi-power systemProtecting the cost;
constructing a double-layer optimized configuration planning model of the multi-power-supply system, namely transmitting a multi-type power supply capacity configuration scheme to a lower-layer model for an upper-layer optimized model, generating an operation scheduling plan with the optimal cost by the lower-layer model according to the power supply capacity, and returning a minimum cost result to the upper-layer model; the upper layer model optimizes the capacity configuration according to the return result;
considering the offshore wind power prediction error, the robust optimization process of the lower layer model is as follows:
Figure FDA0003822593480000038
wherein x is the traditional power output; epsilon is a predicted value of the offshore wind power, belongs to an uncertain set U, and U is a bounded set; f is an objective function; g is a constraint function.
2. The method for scheduling capacity configuration of multiple power supply systems considering offshore wind power access according to claim 1, wherein the multiple power supply system model in step (1) is a multiple power supply system including thermal power generation, gas turbine power generation and pumped storage.
3. The method for scheduling capacity allocation of multiple power supply systems considering offshore wind power access according to claim 1, wherein the step (2) of establishing the model of the multiple power supply system construction and maintenance cost is to allocate the model of the multiple power supply systems to each scheduling period in the service life by discount rate, and the implementation process is as follows:
Figure FDA0003822593480000041
wherein:
Figure FDA0003822593480000042
wherein the content of the first and second substances,
Figure FDA0003822593480000043
is a system
Figure FDA0003822593480000044
The unit cost of manufacture;
Figure FDA0003822593480000045
is a system
Figure FDA0003822593480000046
The capacity of (a);
Figure FDA0003822593480000047
is a system
Figure FDA0003822593480000048
The service life of the internal unit; lambda is the discount rate;
Figure FDA0003822593480000049
is a system of
Figure FDA00038225934800000410
The ratio of maintenance cost to construction cost.
4. A multi-power-supply-system capacity allocation scheduling device considering offshore wind power access, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the computer program, when loaded into the processor, implements the multi-power-supply-system capacity allocation scheduling method considering offshore wind power access according to any one of claims 1 to 3.
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