CN113364051A - Capacity allocation scheduling method and device of multi-power-supply system considering offshore wind power access - Google Patents

Capacity allocation scheduling method and device of multi-power-supply system considering offshore wind power access Download PDF

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CN113364051A
CN113364051A CN202110692975.5A CN202110692975A CN113364051A CN 113364051 A CN113364051 A CN 113364051A CN 202110692975 A CN202110692975 A CN 202110692975A CN 113364051 A CN113364051 A CN 113364051A
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power
offshore wind
model
cost
wind power
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CN113364051B (en
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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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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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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a capacity configuration scheduling method and device of a multi-power-supply system considering offshore wind power access, which comprises the steps of firstly, selecting power supply and energy storage types according to output characteristics and complementary relations, and establishing a multi-power-supply system model considering offshore wind power cluster access; then establishing a multi-power 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 continuously accelerated, 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 intertidal zones, 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 relatively smaller, and planning and design considerations 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 purpose of the invention is as follows: 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 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 the generator set in the 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) 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 (a);
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:
COP(t)＝CFO(t)+CTO(t)+CPO(t)
in the formula, CFO、CTORespectively a t-time thermal power system and fuel gasThe turbine power generation system burn-up cost; cPOThe 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 isi、bi、ciThe correlation coefficient of the operation cost of the thermal power generating unit i is obtained; p is a radical ofnIs the unit burn-up cost of natural gas; eta is the generating efficiency of the gas turbine set; n is a radical ofG、NTRespectively 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; sgen,k、spum,kStarting costs of power generation and water pumping of the water pumping and energy storage unit k are respectively set;
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 ofHIs the total number of the pumping and storage 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 wind power prediction value of the offshore wind farm w at the t period is obtained;
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 is obtained; n is a radical ofWThe 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 abandoning 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 period t;
Figure BDA0003126888670000042
respectively obtaining the minimum and maximum output power allowed by the thermal power generating unit i;
Figure BDA0003126888670000043
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 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 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 BDA00031268886700000413
respectively the minimum and maximum generating power of the pumped storage group within the allowable range of k
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, WtThe 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; wmin、WmaxThe minimum and maximum water quantities of the upper reservoir are respectively.
Further, the objective function in step (4) is:
Figure BDA00031268886700000417
in the formula, COP(t) cost of operating the multiple power supply system, CIMThe 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 beneficial effects that: 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, as shown in figure 1, a power supply and energy storage model in the multi-power-supply system are determined by considering offshore wind power access; 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 aiming at an electric power system accessed by an offshore wind power large-scale 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:
COP(t)＝CFO(t)+CTO(t)+CPO(t)
in the formula, CFO、CTORespectively generating power for a thermal power system and a gas turbine in a time period tSystem burn-up cost; cPOAnd 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 isi、bi、ciThe correlation coefficient of the operation cost of the thermal power generating unit i is obtained; p is a radical ofnIs the unit burn-up cost of natural gas; eta is the generating efficiency of the gas turbine set; n is a radical ofG、NTRespectively 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; sgen,k、spum,kStarting costs of power generation and water pumping of the water pumping and energy storage unit k are respectively set;
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 ofHIs the total number of the pumping unit.
And 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
tthe 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 ofG、NT、NH、NWRespectively counting the thermal power generating units, the gas turbine units, the pumping and storage units and the offshore wind turbine units; 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 abandon 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 an operating state in a period t;
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 and maximum generating power of the pumped storage group k within the allowable range;
Figure BDA00031268886700000810
the minimum pumping power and the maximum pumping power are respectively 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, WtThe 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; wmin、WmaxThe 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 an objective 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, COP(t) cost of operating the multiple power supply system, CIMCost 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 allocation 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 (4) calling a self-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 a self-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 small, the local space searching capability of the algorithm can be improved, but the capability of searching a new area is weak, 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, 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.
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 an 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 (9)

1. A capacity configuration scheduling method for 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 the generator set in the 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) and outputting a power supply capacity configuration scheme with optimal cost and an operation planning result.
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 FDA0003126888660000011
wherein:
Figure FDA0003126888660000012
belongs to the field of thermal power systems, gas turbine power generation systems and pumped storage systems
Wherein the content of the first and second substances,
Figure FDA0003126888660000013
is a system
Figure FDA0003126888660000014
The unit cost of manufacture;
Figure FDA0003126888660000015
is a system
Figure FDA0003126888660000016
The capacity of (a);
Figure FDA0003126888660000017
is a system
Figure FDA0003126888660000018
The service life of the internal unit; lambda is the discount rate;
Figure FDA0003126888660000019
is a system
Figure FDA00031268886600000110
The ratio of maintenance cost to construction cost.
4. The method for capacity allocation scheduling of a multi-power-supply system considering offshore wind power access according to claim 1, wherein the step (2) of establishing the operation cost model of the multi-power-supply system is to respectively establish a fuel consumption cost model and a starting cost model of a unit in the system, and the operation cost of the multi-power-supply system is as follows:
COP(t)＝CFO(t)+CTO(t)+CPO(t)
in the formula, CFO、CTOThe fuel consumption costs of a thermal power system and a gas turbine power generation system in the period t are respectively; cPOThe starting cost of the pumped storage system is t time period;
the system burn-up cost model is:
Figure FDA0003126888660000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003126888660000022
respectively outputting power of the thermal power generating unit i and the gas turbine unit g in a time period t; a isi、bi、ciThe correlation coefficient of the operation cost of the thermal power generating unit i is obtained; p is a radical ofnIs the unit burn-up cost of natural gas; eta is the generating efficiency of the gas turbine set; n is a radical ofG、NTRespectively the total number of the thermal power generating unit and the gas turbine unit;
the system startup cost model is:
Figure FDA0003126888660000023
Figure FDA0003126888660000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003126888660000025
starting costs of the pumped storage unit k during power generation and water pumping in the time period t are respectively set; sgen,k、spum,kStarting costs of power generation and water pumping of the water pumping and energy storage unit k are respectively set;
Figure FDA0003126888660000026
representing that the pumped storage unit k is in a power generation state in a time period t;
Figure FDA0003126888660000027
indicating that the pumped storage unit k is in a pumped state in the time period t; n is a radical ofHIs the total number of the pumping unit.
5. The capacity allocation scheduling method of the multi-power-supply system considering offshore wind power access according to claim 1, wherein the operation constraint conditions of the generator set in the multi-power-supply system in the step (3) are as follows:
Figure FDA0003126888660000028
in the formula (I), the compound is shown in the specification,
Figure FDA0003126888660000029
the power generation and pumping power of the pumped storage unit k in the time period t are respectively;
Figure FDA00031268886600000210
the wind power prediction value of the offshore wind farm w at the t period is obtained;
Figure FDA00031268886600000211
the abandoned wind power of the offshore wind farm w in the period t; pt dFor time t period load sectionThe active power predicted value of the point d; n is a radical ofWThe 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 FDA00031268886600000212
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 FDA00031268886600000213
Figure FDA0003126888660000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003126888660000032
for the starting and stopping conditions of the thermal power generating unit i in the time period t,
Figure FDA0003126888660000033
indicating that the thermal power generating unit i is in a shutdown state during the period t,
Figure FDA0003126888660000034
indicating that the thermal power generating unit i is in an operating state in a period t;
Figure FDA0003126888660000035
respectively obtaining the minimum and maximum output power allowed by the thermal power generating unit i;
Figure FDA0003126888660000036
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 FDA0003126888660000037
Figure FDA0003126888660000038
in the formula (I), the compound is shown in the specification,
Figure FDA0003126888660000039
is the starting and stopping condition of the gas turbine set g in the time period t,
Figure FDA00031268886600000310
indicating that the gas turbine group g is in a stopped state for a period t,
Figure FDA00031268886600000311
indicating that the gas turbine unit g is in an operating state in a time period t;
Figure FDA00031268886600000312
respectively the minimum and maximum output power allowed by the gas turbine unit g;
Figure FDA00031268886600000313
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 FDA00031268886600000314
in the formula (I), the compound is shown in the specification,
Figure FDA00031268886600000315
whether the pumped storage unit k is in the time interval of tThe power generation and water pumping states are represented by 1, and the corresponding state is represented by 0;
Figure FDA00031268886600000316
respectively the minimum and maximum generating power of the pumped storage group k within the allowable range;
Figure FDA00031268886600000317
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 FDA00031268886600000318
in the formula, WtThe water quantity of the upper reservoir is t time period;
Figure FDA00031268886600000319
average water quantity/electric quantity conversion coefficients of water pumping and power generation states in a time period t are respectively set; wmin、WmaxThe minimum and maximum water quantities of the upper reservoir are respectively.
6. The method for allocating and scheduling the capacity of the multi-power-supply system considering offshore wind power access according to claim 1, wherein the objective function in the step (4) is as follows:
Figure FDA00031268886600000320
in the formula, COP(t) cost of operating the multiple power supply system, CIMThe construction and maintenance cost of a multi-power system is reduced.
7. The method for scheduling multi-power-supply-system capacity allocation considering offshore wind power access according to claim 1, wherein the building of the multi-power-supply-system double-layer optimized allocation planning model in the step (4) is that the upper-layer optimized model transmits a multi-type power supply capacity allocation scheme 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 returns the minimum cost result to the upper-layer model; and the upper layer model optimizes the capacity configuration according to the return result.
8. The method for scheduling capacity allocation of multiple power supply systems considering offshore wind power access according to claim 1, wherein the step (4) of considering the prediction error of offshore wind power, and performing a robust optimization process on a lower model comprises the following steps:
Figure FDA0003126888660000041
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.
9. 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 8.
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