CN114498638A - Source network load storage coordination planning method and system considering source load bilateral uncertainty - Google Patents

Source network load storage coordination planning method and system considering source load bilateral uncertainty Download PDF

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CN114498638A
CN114498638A CN202210387577.7A CN202210387577A CN114498638A CN 114498638 A CN114498638 A CN 114498638A CN 202210387577 A CN202210387577 A CN 202210387577A CN 114498638 A CN114498638 A CN 114498638A
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capacity
uncertainty
optimization model
load
layer
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陈会员
张华�
戴奇奇
王欣
王敏
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
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Abstract

The invention discloses a source network load storage coordination planning method and a source network load storage coordination planning system considering source load bilateral uncertainty, wherein the method comprises the following steps: optimizing the capacity configuration of the power supply unit and the capacity configuration of the energy storage equipment in the region according to the constructed upper-layer capacity optimization model to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment; and inputting the initial installed capacity of the power unit and the initial installed capacity of the energy storage equipment into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of the resource at the demand side, so as to output an optimized operation scheme and operation cost of the power system. On the basis of considering user interruptible load and transferable load, a user demand response model is constructed, and uncertainty of user response behavior is represented by rigid load uncertainty.

Description

Source network load storage coordination planning method and system considering source load bilateral uncertainty
Technical Field
The invention belongs to the technical field of power system analysis, and particularly relates to a source network load storage coordination planning method and system considering source load bilateral uncertainty.
Background
In recent years, with the continuous promotion of energy transformation, the installed occupation ratio of new energy mainly comprising wind power and photovoltaic is remarkably improved. Because the output of new energy such as wind power, photovoltaic and the like has the characteristics of randomness, intermittence and the like, large-scale new energy grid connection brings great challenges to the safe and stable operation of the power system, and higher requirements are provided for the flexibility of the power system. The traditional power supply unit, the energy storage equipment and the demand response resource have the effects of improving the regulation capacity of the power system, stabilizing the fluctuation of new energy, promoting the consumption of the new energy and the like.
Therefore, in order to ensure the reliable supply of energy and power, the integrated development of source network charge storage needs to be promoted comprehensively, new energy, conventional power supplies and energy storage equipment are coordinated and planned, and demand-side management is promoted vigorously, so that the low-carbon efficient development of a power system is realized, but the accuracy of the current source network charge storage planning is not high.
Disclosure of Invention
The invention provides a source network load-storage coordination planning method and system considering source load bilateral uncertainty, which are used for solving at least one of the technical problems.
In a first aspect, the present invention provides a source network load-storage coordination planning method considering uncertainty on both sides of a source load, including: optimizing the capacity configuration of a power supply unit and the capacity configuration of energy storage equipment in an area according to the constructed upper-layer capacity optimization model to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment, wherein the upper-layer capacity optimization model takes the annual value of system planning cost and the like as the minimum optimization target; inputting the initial installed capacity of the power unit and the initial installed capacity of the energy storage equipment into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, so as to output an optimized operation scheme and an operation cost of the power system, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target; and inputting the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, updating the installed capacity of the power unit and the installed capacity of the energy storage equipment, inputting the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterating the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
In a second aspect, the present invention provides a source network load-storage coordination planning system for considering uncertainty on both sides of a source load, including: the optimization module is configured to optimize the capacity configuration of the power supply unit and the capacity configuration of the energy storage equipment in the region according to the constructed upper-layer capacity optimization model so as to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment, wherein the upper-layer capacity optimization model takes the minimum annual value of system planning cost and the like as an optimization target; the output module is configured to input the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage device into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, so that an optimized operation scheme and an operation cost of an output power system are achieved, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target; and the iteration module is configured to input the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, update the installed capacity of the power unit and the installed capacity of the energy storage equipment, input the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterate between the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
In a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the source net load storage coordination planning method of any of the embodiments of the present invention that account for source-to-load bilateral uncertainty.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to execute the steps of the source grid load storage coordination planning method considering source-to-load bilateral uncertainty according to any embodiment of the present invention.
According to the source network load-storage coordination planning method and system considering source load bilateral uncertainty, on the basis of considering user interruptible load and transferable load, user incentive type demand response and price type demand response models are established, and ambiguity of user demand response is considered, and ambiguity parameters are adopted to represent uncertainty of user response behaviors, so that accuracy of source network load-storage planning is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a source network load-storage coordination planning method for accounting for source-load bilateral uncertainty according to an embodiment of the present invention;
fig. 2 is a block diagram of a source network load-storage coordination planning system for accounting for source-load bilateral uncertainty according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flow chart of a source network charge-storage coordination planning method for accounting for source-charge bilateral uncertainty according to the present application is shown.
As shown in fig. 1, the source network load-storage coordination planning method considering source-load bilateral uncertainty specifically includes the following steps:
and S101, optimizing the capacity configuration of the power supply unit and the capacity configuration of the energy storage equipment in the region according to the constructed upper-layer capacity optimization model to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment, wherein the upper-layer capacity optimization model takes the minimum annual value of system planning cost and the like as an optimization target.
In this embodiment, the capacity configuration of various power supply units and energy storage devices in a region is optimized with the goal of minimizing the annual value such as system planning cost. The system planning cost includes system investment cost and maintenance cost. The expression aiming at the minimum annual value of the system planning cost is as follows:
Figure 32259DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 562597DEST_PATH_IMAGE002
in order to minimize the overall cost of the system,
Figure 440424DEST_PATH_IMAGE003
the investment cost and the maintenance cost are respectively.
Figure 321792DEST_PATH_IMAGE004
，
In the formula (I), the compound is shown in the specification,
Figure 693999DEST_PATH_IMAGE005
respectively unit investment costs of thermal power, hydropower, wind power, photovoltaic and energy storage,
Figure 344423DEST_PATH_IMAGE006
respectively the installed capacities of thermal power, hydropower, wind power, photovoltaic and energy storage,
Figure 393150DEST_PATH_IMAGE007
the coefficients of the debt fund of thermal power, hydropower, wind power, photovoltaic and energy storage are respectively;
Figure 761815DEST_PATH_IMAGE008
，
in the formula (I), the compound is shown in the specification,
Figure 905089DEST_PATH_IMAGE009
the unit investment costs of thermal power, hydropower, wind power, photovoltaic and energy storage are respectively.
It should be noted that, the expression of the installed capacity constraint is:
Figure 675599DEST_PATH_IMAGE010
，
in the formula (I), the compound is shown in the specification,
Figure 629648DEST_PATH_IMAGE011
the upper limit of the installed capacity of thermal power, hydropower, wind power, photovoltaic, electrochemical energy storage and water pumping and energy storage,
Figure 485609DEST_PATH_IMAGE012
respectively is the lower limit of the installed capacity of wind power, photovoltaic and electrochemical energy storage;
the installed capacity and energy storage resources of a power supply unit built by the system need to be larger than the maximum load of the system planning level year under the condition of considering a certain confidence coefficient. The expression for the power constraint is:
Figure 199618DEST_PATH_IMAGE013
，
in the formula (I), the compound is shown in the specification,
Figure 559055DEST_PATH_IMAGE014
thermal power, hydroelectric power, wind power, photovoltaic power and energy storage are respectively measured by an equivalent capacity coefficient method,
Figure 949585DEST_PATH_IMAGE015
the capacity of the foreign electrotransport channel,
Figure 27263DEST_PATH_IMAGE016
being confidence in the capacity for incoming calls outside the zone,
Figure 466334DEST_PATH_IMAGE017
in order to plan the annual system maximum load,
Figure 514232DEST_PATH_IMAGE018
in response to the capacity of the resource,
Figure 747767DEST_PATH_IMAGE019
is the confidence of the response resource;
the power generation amount of all the generator sets in the planning horizontal year of the system is larger than the power demand in the planning horizontal year. The expression of the power constraint is:
Figure 437374DEST_PATH_IMAGE020
，
in the formula (I), the compound is shown in the specification,
Figure 617820DEST_PATH_IMAGE021
the annual utilization hours of thermal power, hydroelectric power, wind power and photovoltaic power respectively,
Figure 827216DEST_PATH_IMAGE022
the demand of electric quantity for planning year;
the expression of the new energy permeability constraint is as follows:
Figure 434914DEST_PATH_IMAGE023
，
in the formula (I), the compound is shown in the specification,
Figure 611818DEST_PATH_IMAGE024
the permeability of new energy.
And S102, inputting the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, and outputting an optimized operation scheme and an optimized operation cost of the power system, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target.
In this embodiment, the upper-layer capacity optimization model determines the capacity configuration of various power units and energy storage devices of the regional power system. And the lower-layer operation optimization model optimizes and schedules various flexible resources of the power system on the basis of the capacity optimization of the upper-layer capacity optimization model by taking the minimization of the operation cost as a target. The lower-layer operation optimization model takes the expression that the operation cost is minimized as an optimization target as follows:
Figure 330375DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 892812DEST_PATH_IMAGE026
in order to minimize the cost of operating the system,
Figure 671413DEST_PATH_IMAGE027
in order to be a cost of the fuel,
Figure 335612DEST_PATH_IMAGE028
in order to save the cost of electricity purchasing from the outside,
Figure 857860DEST_PATH_IMAGE029
the cost is scheduled for the demand response and,
Figure 41848DEST_PATH_IMAGE030
is the carbon emission cost;
the expression for calculating the fuel cost is:
Figure 991350DEST_PATH_IMAGE031
，
in the formula (I), the compound is shown in the specification,
Figure 877266DEST_PATH_IMAGE032
in order to be the market price of the fuel,
Figure 999943DEST_PATH_IMAGE033
is the unit output fuel consumption of the thermal power generating unit,
Figure 163071DEST_PATH_IMAGE034
the output of the thermal power generating unit at the moment T is shown, and T is a scheduling period;
the expression for calculating the electricity purchasing cost of the incoming call is as follows:
Figure 660305DEST_PATH_IMAGE035
，
in the formula (I), the compound is shown in the specification,
Figure 174463DEST_PATH_IMAGE036
、
Figure 163147DEST_PATH_IMAGE037
respectively is the electricity price and the output of an incoming call in an extra-high voltage direct current transmission project;
the expression for calculating the scheduling cost of the demand response resources is as follows:
Figure 180782DEST_PATH_IMAGE038
，
in the formula (I), the compound is shown in the specification,
Figure 613031DEST_PATH_IMAGE039
as a subsidizing criterion for the incentive-type demand response,
Figure 83327DEST_PATH_IMAGE040
in response to the IL capacity for the user plan,
Figure 141282DEST_PATH_IMAGE041
in order that the load can be interrupted,
Figure 747843DEST_PATH_IMAGE042
is the scheduling duration;
the expression for calculating the carbon emission cost is:
Figure 849529DEST_PATH_IMAGE043
，
in the formula (I), the compound is shown in the specification,
Figure 72700DEST_PATH_IMAGE044
the standard for the charge of the carbon tax is,
Figure 403188DEST_PATH_IMAGE045
the carbon emission per output of thermal power, wind power and photovoltaic power respectively,
Figure 395414DEST_PATH_IMAGE046
the actual output of the wind power is obtained,
Figure 169466DEST_PATH_IMAGE047
actual photovoltaic output is obtained;
it should be noted that the constraint conditions of the lower-layer operation optimization model include a power balance constraint, a conventional power supply unit output constraint, an energy storage output constraint, a line transmission power constraint, an excitation type DR constraint, and a standby constraint.
(1) And power balance constraint:
Figure 411092DEST_PATH_IMAGE048
，
in the formula (I), the compound is shown in the specification,
Figure 420636DEST_PATH_IMAGE049
the output of the hydropower at the moment t,
Figure 657582DEST_PATH_IMAGE050
for the discharging power and the charging power of the energy storage device at the time t,
Figure 461590DEST_PATH_IMAGE051
in order to satisfy the confidence of the power balance constraint,
Figure 36185DEST_PATH_IMAGE052
participating in the load demand after the price type demand response for the user;
(2) the output constraint of the conventional power supply unit:
1) thermal power constraint
The output of the thermal power generating unit needs to meet output power constraint, climbing rate constraint and start-stop time constraint.
Figure 583841DEST_PATH_IMAGE053
，
In the formula (I), the compound is shown in the specification,
Figure 409714DEST_PATH_IMAGE054
the output of thermal power at the moment t-1,
Figure 384623DEST_PATH_IMAGE055
、
Figure 945049DEST_PATH_IMAGE056
respectively for the down regulation and the up regulation of the climbing speed of the thermal power,
Figure 296396DEST_PATH_IMAGE057
for the on-line start-up capacity of the thermal power at time t,
Figure 242355DEST_PATH_IMAGE058
is the online starting capacity of the thermal power at the moment t-1,
Figure 653745DEST_PATH_IMAGE059
is the minimum output proportion of the thermal power,
Figure 357259DEST_PATH_IMAGE060
respectively the starting capacity and the stopping capacity of the thermal power at the time t,
Figure 620619DEST_PATH_IMAGE061
respectively the shortest starting time and the shortest stopping time of the thermal power,
Figure 562030DEST_PATH_IMAGE062
for generating heat
Figure 737796DEST_PATH_IMAGE063
The capacity of the boot-up at the moment,
Figure 397448DEST_PATH_IMAGE064
for generating heat
Figure 700384DEST_PATH_IMAGE063
(ii) the outage capacity at that moment;
2) hydro-power generating unit restraint
The hydroelectric generating set has the operation characteristics of strong climbing capacity, short start-stop time, zero pollution and the like. The output is limited by the maximum and minimum output, the maximum output mainly depends on the characteristics of the river water coming season, and the minimum output mainly depends on the vibration area requirement of the hydroelectric generating set. The hydro-power generating unit output constraint condition can be expressed as:
Figure 496302DEST_PATH_IMAGE065
，
in the formula (I), the compound is shown in the specification,
Figure 374128DEST_PATH_IMAGE066
、
Figure 255496DEST_PATH_IMAGE067
respectively is the minimum output proportion and the maximum output proportion of the hydropower at the time t,
Figure 283495DEST_PATH_IMAGE068
the power is the output of the hydropower at the time t,
Figure 310750DEST_PATH_IMAGE069
capacity for water installed;
(3) energy storage output restraint
The charging and discharging power of the energy storage device in the operation process is subjected to energy constraint and power constraint.
Figure 234844DEST_PATH_IMAGE070
，
In the formula (I), the compound is shown in the specification,
Figure 728142DEST_PATH_IMAGE071
the states of charge of the energy storage device at times t +1 and t respectively,
Figure 763094DEST_PATH_IMAGE072
the charging and discharging states of the energy storage device are respectively 0-1 variable,
Figure 143391DEST_PATH_IMAGE073
in order to schedule the time duration,
Figure 972807DEST_PATH_IMAGE074
is the self-loss rate of the energy storage device,
Figure 218981DEST_PATH_IMAGE075
the charging and discharging efficiencies of the energy storage device are respectively,
Figure 526465DEST_PATH_IMAGE076
is the minimum state of charge of the energy storage device,
Figure 525383DEST_PATH_IMAGE077
is the maximum state of charge of the energy storage device,
Figure 791279DEST_PATH_IMAGE078
、
Figure 993590DEST_PATH_IMAGE079
respectively the charging power and the discharging power of the energy storage equipment at the time t,
Figure 635924DEST_PATH_IMAGE080
is the power rating of the energy storage device,
Figure 912185DEST_PATH_IMAGE081
in order to be the installed capacity of the energy storage device,
Figure 224349DEST_PATH_IMAGE082
for the state of charge of the energy storage device at time t,
Figure 789322DEST_PATH_IMAGE083
the state of charge of the energy storage device at time 1,
Figure 828822DEST_PATH_IMAGE084
the state of charge of the energy storage device at the 24 th moment;
(4) line delivered power constraint
The multi-area power grid interconnection generally adopts a line protocol power transmission mode, and the transmission power of a line should not exceed the transmission capacity. Thus, the line transmit power constraint is:
Figure 162852DEST_PATH_IMAGE085
，
in the formula (I), the compound is shown in the specification,
Figure 424680DEST_PATH_IMAGE086
transmitting power for a specified line;
(5) incentive type DR constraint
The excitation type DR should satisfy the response time constraint:
Figure 211370DEST_PATH_IMAGE087
，
in the formula (I), the compound is shown in the specification,
Figure 54561DEST_PATH_IMAGE088
the state is invoked for the resource on the demand side,
Figure 508676DEST_PATH_IMAGE089
is the maximum response time length;
(6) standby restraint
Figure 84014DEST_PATH_IMAGE090
，
In the formula (I), the compound is shown in the specification,
Figure 498946DEST_PATH_IMAGE091
is the upper limit of the output of thermal power and hydropower at the moment t,
Figure 21194DEST_PATH_IMAGE092
for the backup demand of the power system at time t,
Figure 454450DEST_PATH_IMAGE093
、
Figure 403951DEST_PATH_IMAGE079
respectively the charging power and the discharging power of the energy storage equipment at the time t,
Figure 539135DEST_PATH_IMAGE094
is the actual value of the wind power output at the moment t,
Figure 599495DEST_PATH_IMAGE095
is the actual value of the photovoltaic output at the moment t,
Figure 887257DEST_PATH_IMAGE052
for the user to participate in the load demand after the price type demand response,
Figure 7660DEST_PATH_IMAGE096
for user participationResponse capacity of the incentive type demand response.
It should be noted that, the actual values of the wind power and the photovoltaic output are regarded as the sum of the predicted value and the predicted value error, and the specific expression is as follows:
Figure 397184DEST_PATH_IMAGE097
，
in the formula (I), the compound is shown in the specification,
Figure 57972DEST_PATH_IMAGE098
、
Figure 75607DEST_PATH_IMAGE099
are respectively as
Figure 757124DEST_PATH_IMAGE100
The actual output of wind power at any moment,
Figure 492999DEST_PATH_IMAGE100
The actual photovoltaic output is generated at any moment,
Figure 271992DEST_PATH_IMAGE101
、
Figure 144133DEST_PATH_IMAGE102
are respectively as
Figure 996552DEST_PATH_IMAGE100
The predicted output of wind power at any moment,
Figure 954143DEST_PATH_IMAGE100
The photovoltaic predicted output at that moment,
Figure 300942DEST_PATH_IMAGE103
、
Figure 293169DEST_PATH_IMAGE104
respectively a wind power prediction error and a photovoltaic prediction error;
the prediction errors of the wind power and the photovoltaic are subjected to normal distribution with the mean value of 0, and the standard deviation of the wind power and the photovoltaic is as follows:
Figure 50909DEST_PATH_IMAGE105
，
in the formula (I), the compound is shown in the specification,
Figure 761376DEST_PATH_IMAGE106
respectively are standard deviations of wind power prediction errors and photovoltaic prediction errors,
Figure 567658DEST_PATH_IMAGE107
respectively the installed capacities of wind power and photovoltaic power;
the demand side resource uncertainty comprises price type demand response uncertainty and incentive type demand response uncertainty;
the expression of the uncertainty of the price type demand response is as follows:
Figure 788293DEST_PATH_IMAGE108
，
in the formula (I), the compound is shown in the specification,
Figure 857880DEST_PATH_IMAGE109
in order to be a fuzzy variable, the fuzzy variable,
Figure 914698DEST_PATH_IMAGE110
the slope of the load transfer rate in the linear region along with the change of the peak-to-valley electricity price difference,
Figure 727933DEST_PATH_IMAGE111
the difference in the electricity price is the peak-to-valley electricity price,
Figure 570118DEST_PATH_IMAGE112
、
Figure 545028DEST_PATH_IMAGE113
peak-to-valley electricity price differences corresponding to dead zone inflection points and saturated zone inflection points respectively,
Figure 354721DEST_PATH_IMAGE114
upper limit of peak-to-valley load transfer rate，
Figure 706068DEST_PATH_IMAGE115
Maximum deviation value of peak-to-valley load transfer rate;
the expression of the excitation-type demand response uncertainty is:
Figure 904224DEST_PATH_IMAGE116
，
in the formula (I), the compound is shown in the specification,
Figure 784455DEST_PATH_IMAGE040
in response to the IL capacity for the user plan,
Figure 815865DEST_PATH_IMAGE117
maximum response IL capacity for the user.
Step S103, inputting the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, updating the installed capacity of the power unit and the installed capacity of the energy storage equipment, inputting the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterating the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
In this embodiment, a particle swarm optimization algorithm is considered and a double-layer planning model is solved by means of a layered iteration idea, that is, an upper-layer capacity optimization model is solved by using a PSO algorithm, a lower-layer operation optimization model is solved by using a conventional optimization method, iteration is repeated between the upper-layer capacity optimization model and the lower-layer operation optimization model, and finally, the optimal solution of the double-layer planning problem is gradually approximated. The solving process is concretely as follows:
step 1, initializing parameters in a PSO algorithm, and randomly generating installed capacities of various power supply units and energy storage equipment to form an initial particle swarm;
step 2, substituting the initialized particle position (namely the solution of the upper model) into the lower model, and calling a CPLEX solver to solve the lower model to obtain the optimal solution of the lower model;
and 3, feeding back the lower-layer operation scheme and the operation cost thereof to the upper layer, calculating the fitness value of each particle, comparing the fitness values of the particles, updating the position (solution of the upper-layer model) and the fitness function of the particles, and updating the local optimization of each particle and the global optimization of the particle swarm.
And 4, judging whether the iteration times are larger than the maximum iteration times, if not, repeating the iteration processes of the steps 2-3 until the conditions are finally met, and obtaining the optimal solution of the source, load and storage scheduling plan.
In summary, according to the method, wind power and photovoltaic new energy are considered for source side uncertainty, and source side randomness is described by considering a new energy output power prediction error value; aiming at load side uncertainty, based on the psychological principle of consumers, modeling is carried out on user demand response uncertainty through fuzzy parameters, a user price type demand response and incentive type demand response model considering uncertainty is constructed, and psychological factors of users participating in demand response are fully considered. Therefore, on the basis of considering user interruptible load and transferable load, a user incentive type demand response and price type demand response model is constructed, ambiguity of user demand response is considered, ambiguity parameters are adopted to represent uncertainty of user response behaviors, and accuracy of source network load storage planning is effectively improved.
Referring to fig. 2, a block diagram of a source network charge-storage coordination planning system for accounting for source-charge bilateral uncertainty according to the present application is shown.
As shown in fig. 2, the source grid load-storage coordination planning system 200 includes an optimization module 210, an output module 220, and an iteration module 230.
The optimization module 210 is configured to optimize the capacity configuration of the power supply unit and the capacity configuration of the energy storage device in the area according to the constructed upper-layer capacity optimization model, so that the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage device are obtained, wherein the upper-layer capacity optimization model takes the annual value of the system planning cost as the minimum optimization target; the output module 220 is configured to input the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage device into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, so as to output an optimized operation scheme and an optimized operation cost of the power system, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target; the iteration module 230 is configured to input the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, update the installed capacity of the power unit and the installed capacity of the energy storage device, input the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterate between the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
It should be understood that the modules depicted in fig. 2 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 2, and are not described again here.
In still other embodiments, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to execute a source grid load storage coordination planning method in any of the above method embodiments, which accounts for source-load bilateral uncertainty;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
optimizing the capacity configuration of a power supply unit and the capacity configuration of energy storage equipment in an area according to the constructed upper-layer capacity optimization model to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment, wherein the upper-layer capacity optimization model takes the annual value of system planning cost and the like as the minimum optimization target;
inputting the initial installed capacity of the power unit and the initial installed capacity of the energy storage equipment into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, so as to output an optimized operation scheme and an operation cost of the power system, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target;
and inputting the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, updating the installed capacity of the power unit and the installed capacity of the energy storage equipment, inputting the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterating the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of a source network charge-storage coordination planning system that accounts for source-to-charge bilateral uncertainty, and the like. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes memory remotely located from the processor, and the remote memory may be networked to a source grid charge-storage coordination planning system that accounts for source-to-charge double-sided uncertainty. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 3. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running the nonvolatile software programs, instructions and modules stored in the memory 320, that is, the source network and storage coordination planning method of the above method embodiment, which accounts for source-to-load bilateral uncertainty, is implemented. The input device 330 can receive the input number or character information and generate the key signal input related to the user setting and function control of the source net charge-storage coordination planning system which takes the uncertainty of both sides of the source charge into account. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to a source network load-store coordination planning system that accounts for uncertainty on both sides of a source load, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
optimizing the capacity configuration of a power supply unit and the capacity configuration of energy storage equipment in an area according to the constructed upper-layer capacity optimization model to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment, wherein the upper-layer capacity optimization model takes the annual value of system planning cost and the like as the minimum optimization target;
inputting the initial installed capacity of the power unit and the initial installed capacity of the energy storage equipment into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, so as to output an optimized operation scheme and an operation cost of the power system, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target;
and inputting the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, updating the installed capacity of the power unit and the installed capacity of the energy storage equipment, inputting the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterating the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A source network load-storage coordination planning method considering source load bilateral uncertainty is characterized by comprising the following steps:
optimizing the capacity configuration of a power supply unit and the capacity configuration of energy storage equipment in an area according to the constructed upper-layer capacity optimization model to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment, wherein the upper-layer capacity optimization model takes the annual value of system planning cost and the like as the minimum optimization target;
inputting the initial installed capacity of the power unit and the initial installed capacity of the energy storage equipment into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, so as to output an optimized operation scheme and an operation cost of the power system, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target;
and inputting the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, updating the installed capacity of the power unit and the installed capacity of the energy storage equipment, inputting the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterating the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
2. The source grid load-storage coordination planning method considering source-load bilateral uncertainty, according to claim 1, characterized in that the expression of the new energy output uncertainty is:
Figure 449368DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 146934DEST_PATH_IMAGE002
、
Figure 457830DEST_PATH_IMAGE003
are respectively as
Figure 384197DEST_PATH_IMAGE004
The actual output of wind power at any moment,
Figure 729728DEST_PATH_IMAGE004
The actual photovoltaic output is generated at any moment,
Figure 286611DEST_PATH_IMAGE005
、
Figure 835536DEST_PATH_IMAGE006
are respectively as
Figure 565594DEST_PATH_IMAGE004
The predicted output of wind power at any moment,
Figure 765631DEST_PATH_IMAGE004
The photovoltaic predicted output at the moment is obtained,
Figure 290154DEST_PATH_IMAGE007
、
Figure 839557DEST_PATH_IMAGE008
respectively, the prediction error of wind power and the prediction error of photovoltaic.
3. The source grid load-storage coordination planning method considering source-load double-side uncertainty, according to claim 1, characterized in that the demand-side resource uncertainty includes price-type demand response uncertainty and incentive-type demand response uncertainty;
the expression of the uncertainty of the price type demand response is as follows:
Figure 107728DEST_PATH_IMAGE009
，
in the formula (I), the compound is shown in the specification,
Figure 162271DEST_PATH_IMAGE010
in order to be a fuzzy variable, the fuzzy variable,
Figure 123274DEST_PATH_IMAGE011
the slope of the load transfer rate in the linear region along with the change of the peak-to-valley electricity price difference,
Figure 912370DEST_PATH_IMAGE012
the difference in the electricity price is the peak-to-valley electricity price,
Figure 718652DEST_PATH_IMAGE013
、
Figure 893281DEST_PATH_IMAGE014
peak-to-valley electricity price differences corresponding to dead zone inflection points and saturated zone inflection points respectively,
Figure 759606DEST_PATH_IMAGE015
the upper limit of the peak-to-valley load transfer rate,
Figure 268954DEST_PATH_IMAGE016
maximum deviation value of peak-to-valley load transfer rate;
the expression of the excitation-type demand response uncertainty is:
Figure 878927DEST_PATH_IMAGE017
，
in the formula (I), the compound is shown in the specification,
Figure 908062DEST_PATH_IMAGE018
in response to the IL capacity for the user plan,
Figure 210868DEST_PATH_IMAGE019
maximum response IL capacity for the user.
4. The source network load-storage coordination planning method considering source-load bilateral uncertainty is characterized in that an expression of an upper-layer capacity optimization model taking the annual value minimum of system planning cost as an optimization target is as follows:
Figure 443397DEST_PATH_IMAGE020
，
in the formula (I), the compound is shown in the specification,
Figure 857061DEST_PATH_IMAGE021
in order to minimize the overall cost of the system,
Figure 943966DEST_PATH_IMAGE022
the investment cost and the maintenance cost are respectively.
5. The source network load-storage coordination planning method considering source-load bilateral uncertainty is characterized in that the lower-layer operation optimization model takes an expression with the operation cost minimized as an optimization target as follows:
Figure 417672DEST_PATH_IMAGE023
，
in the formula (I), the compound is shown in the specification,
Figure 966859DEST_PATH_IMAGE024
in order to minimize the cost of operating the system,
Figure 918634DEST_PATH_IMAGE025
in order to be a cost of the fuel,
Figure 922362DEST_PATH_IMAGE026
in order to meet the electricity purchasing cost of the external electricity,
Figure 317703DEST_PATH_IMAGE027
the cost is scheduled for the demand response and,
Figure 774092DEST_PATH_IMAGE028
which is a carbon emission cost.
6. A source network load-storage coordination planning system considering source-load bilateral uncertainty is characterized by comprising:
the optimization module is configured to optimize the capacity configuration of the power supply unit and the capacity configuration of the energy storage equipment in the region according to the constructed upper-layer capacity optimization model so as to obtain the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage equipment, wherein the upper-layer capacity optimization model takes the minimum annual value of system planning cost and the like as an optimization target;
the output module is configured to input the initial installed capacity of the power supply unit and the initial installed capacity of the energy storage device into a lower-layer operation optimization model constructed based on the uncertainty of new energy output and the uncertainty of demand-side resources, so that an optimized operation scheme and an operation cost of an output power system are achieved, wherein the lower-layer operation optimization model takes the minimization of the operation cost as an optimization target;
and the iteration module is configured to input the optimized operation scheme and the operation cost into the upper-layer capacity optimization model, update the installed capacity of the power unit and the installed capacity of the energy storage equipment, input the updated installed capacity into the lower-layer operation optimization model, and repeatedly iterate between the upper-layer capacity optimization model and the lower-layer operation optimization model to obtain an optimal solution of the source network load-storage coordination plan.
7. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 5.
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