CN116822908B - Multi-time-scale energy storage planning method and equipment capable of being rapidly solved - Google Patents
Multi-time-scale energy storage planning method and equipment capable of being rapidly solved Download PDFInfo
- Publication number
- CN116822908B CN116822908B CN202311071651.5A CN202311071651A CN116822908B CN 116822908 B CN116822908 B CN 116822908B CN 202311071651 A CN202311071651 A CN 202311071651A CN 116822908 B CN116822908 B CN 116822908B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- time
- day
- long
- planning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 321
- 238000000034 method Methods 0.000 title claims abstract description 83
- 238000010977 unit operation Methods 0.000 claims abstract description 12
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 10
- 230000007774 longterm Effects 0.000 claims description 45
- 238000007599 discharging Methods 0.000 claims description 17
- 230000006870 function Effects 0.000 claims description 17
- 238000012423 maintenance Methods 0.000 claims description 12
- 230000008878 coupling Effects 0.000 claims description 10
- 238000010168 coupling process Methods 0.000 claims description 10
- 238000005859 coupling reaction Methods 0.000 claims description 10
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 7
- 229910052799 carbon Inorganic materials 0.000 claims description 7
- 230000003247 decreasing effect Effects 0.000 claims description 4
- 238000012546 transfer Methods 0.000 description 7
- 230000001932 seasonal effect Effects 0.000 description 6
- 238000005457 optimization Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 230000009194 climbing Effects 0.000 description 3
- 239000000446 fuel Substances 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000005315 distribution function Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012983 electrochemical energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
Abstract
The application relates to a multi-time-scale energy storage planning method capable of being rapidly solved, which comprises the following steps: constructing a multi-time scale energy storage collaborative planning model, wherein the multi-time scale energy storage collaborative planning model comprises an objective function and a plurality of constraint conditions, wherein the objective function is used for minimizing the total cost of the system; the constraint conditions comprise multi-time scale energy storage planning constraint, multi-time scale energy storage operation constraint, power balance constraint, renewable energy source operation constraint and thermal power generating unit operation constraint; and taking a short-time energy storage operation typical scene extracted based on a clustering algorithm as input, and solving an objective function according to the constraint condition to obtain a planning scheme of the short-time energy storage component and the long-time energy storage device.
Description
Technical Field
The application relates to a multi-time-scale energy storage planning method and equipment capable of being rapidly solved, and belongs to the field of energy storage planning.
Background
Considering that the installed share of the thermal power is continuously replaced by wind power and photovoltaic under the background of high-proportion new energy grid connection, the traditional flexible resources are insufficient to meet the flexible regulation requirements of a novel power system, and energy storage becomes the main flexible regulation resource of the future power system for coping with the challenge of aggravation of unbalanced electric power and electric quantity. Short-time energy storage represented by electrochemical energy storage mainly provides services such as frequency modulation, peak shaving, climbing and the like in the sun, and stabilizes the unbalance of the output of new energy sources in a short time scale and the electric quantity of load demands. And long-time energy storage can realize energy transfer in a long time scale, stabilize seasonal electric quantity fluctuation for days, weeks and weeks, and solve the problem of supply and demand mismatch of seasonal electric quantity. Therefore, collaborative planning configuration of long-short-time energy storage provides a solution to the problem of unbalance of different time scales of new energy output and load demand, and has important significance for meeting the flexible demands of different time scales of a high-proportion new energy power system.
The energy storage planning model aims at improving the efficiency, stability and sustainability of an energy system to the greatest extent through reasonably planning and managing energy storage resources. The built model is high in calculation complexity and high in solving difficulty, and the capability of energy transfer in a long-time range can be realized by the long-time energy storage device (LDES). In the prior art, planning configuration and operation of long-time and short-time energy storage are cooperatively optimized by using an hour time division ratio, the model scale is overlarge, and the calculation efficiency is extremely low. Therefore, there is a need for an energy storage planning method that can meet both high accuracy and low solution time.
CN112072655a, "a hybrid energy storage optimizing configuration method of grid-connected wind-storage power generation system," discloses: carrying out frequency domain decomposition on the historical wind power output power, counting high-frequency components and low-frequency components of the historical wind power output power, and determining the rated power of the hybrid energy storage based on a probability distribution function; constructing a full life cycle hybrid energy storage capacity optimization model of the wind power plant with the aim of minimum annual cost net present value and maximum target output satisfaction rate; extracting wind power output power daily typical scenes based on a clustering algorithm, and counting the time duty ratio of each typical scene to be used as an input scene of a full life cycle hybrid energy storage capacity optimization model of a wind power plant; and solving by adopting a multi-objective optimization algorithm to obtain an optimal mixed energy storage capacity configuration scheme of the grid-connected wind power generation system. The method still builds an hour-level energy storage planning model, and the model complexity is high.
CN110350518A, "a power grid energy storage capacity demand assessment method and system for peak shaving", discloses: presetting a plurality of planning schemes containing different energy storage power capacities; performing daily operation simulation on each planning scheme for 365 days in the whole year by using a power system operation simulation technology to obtain the charge and discharge scheduling conditions of the energy storage whole year peak regulation period under each planning scheme; carrying out statistical analysis modeling according to annual energy storage output operation data of each planning scheme to obtain an accumulated probability distribution function of the energy storage capacity of the energy storage equipment, setting expected probability that the energy storage equipment meets the peak shaving requirement of the whole network, and calculating the corresponding energy storage capacity requirement; and finally, calculating a planning scheme corresponding to the minimum value obtained by the comprehensive operation cost of the whole system, namely the optimal planning scheme. Also, the scheme performs daily operation simulation at the hour level, and has high calculation complexity.
Disclosure of Invention
In order to solve the problems in the prior art, the application designs a multi-time-scale energy storage planning method capable of being rapidly solved, which is based on the positioning of different functions of ensuring the daily power supply adequacy of short-time energy storage and taking long-time energy storage as seasonal electric quantity support, the long-time and short-time energy storage is discretized in the long-time and short-time energy storage collaborative planning problem, and the coupling relation between the long-time and short-time energy storage is simplified according to the difference of energy storage energy transfer characteristics of different time scales, so that a multi-time-scale energy storage collaborative planning model aiming at minimizing the total cost of a system is obtained.
In order to achieve the above purpose, the present application adopts the following technical scheme:
the technical scheme is as follows:
a multi-time scale energy storage planning method capable of being solved rapidly comprises the following steps:
constructing a multi-time scale energy storage collaborative planning model, wherein the multi-time scale energy storage collaborative planning model comprises an objective function and a plurality of constraint conditions, wherein the objective function is used for minimizing the total cost of the system;
the constraint conditions comprise multi-time scale energy storage planning constraint, multi-time scale energy storage operation constraint, power balance constraint, renewable energy source operation constraint and thermal power generating unit operation constraint;
the multi-time scale energy storage operation constraint comprises an hour-level short-time energy storage operation constraint and a day-level long-time energy storage operation constraint;
the power balance constraint comprises a first power balance constraint in which typical intra-day-hour-level short-time energy storage and long-time energy storage participate cooperatively, a second power balance constraint in which intra-year-level long-time energy storage participates, and a coupling relation of the first power balance constraint and the second power balance constraint;
and taking a short-time energy storage operation typical scene extracted based on a clustering algorithm as input, and solving an objective function according to the constraint condition to obtain a planning scheme of the short-time energy storage component and the long-time energy storage device.
Further, the total cost of the system includes investment costsEnergy storage operation and maintenance cost->Cost of operationCarbon emission cost->Wind abandon punishment->；
Wherein, the energy storage investment costEnergy storage operation and maintenance cost->Expressed as:
in the method, in the process of the application, 、/>planning a newly increased energy storage capacity for short-time energy storage and long-time energy storage; />Power capacity for short-term energy storage; />、/>Charge and discharge power capacity for long-term energy storage; /> 、/>The unit investment cost of the energy storage capacity, the discharge power capacity and the charge power capacity is respectively; />For the rate of discount, add>The full life cycle of the energy storage system; B. LO is a technical parameter set of short-time energy storage and long-time energy storage respectively; /> 、/> 、/>The unit operation and maintenance costs are respectively the energy storage capacity, the discharge power capacity and the charge power capacity.
Further, the multi-time scale energy storage planning constraint includes:
setting a value range of the energy storage power and the energy storage capacity newly added in the short-time energy storage planning;
setting a charging power capacity, a discharging power capacity and a value range of the energy storage capacity which are newly added in the long-time energy storage planning.
Further, the daily long-term energy storage operation constraint is expressed as:
in the method, in the process of the application,the charge state of the residual electric quantity of the long-time energy storage device on the r day; />Respectively long-term energy storage device->Discharge power; /> 、/>Respectively charging and discharging efficiency of the long-time energy storage device; LO is a technical parameter set for long-time energy storage;N R for planning the number of days of the year; /> 、/> 、/>Planning newly increased energy storage capacity, discharge power capacity and charging power capacity for the long-time energy storage system respectively; />Representing the charge and discharge state of the long-term energy storage device on the r day; />Is the initial charge ratio of the long-term energy storage device.
Further, the first power balance constraint is expressed as:
in the method, in the process of the application,is a typical day; />Is typical day +.>Time->The output of the thermal power generating unit; />The number of the thermal power generating units;representing typical day +.>Time->Actual output of the wind turbine generator; />The number of the wind turbine generators is the number of the wind turbine generators; /> 、/>Respectively represent typical day +.>Time->Charging and discharging power of the short-time energy storage device; /> 、/>Respectively representing the charge and discharge power of the energy storage device at the typical day d time t long time; />The number of the short-time energy storage devices and the number of the long-time energy storage devices are respectively; />Is typical day +.>Time->Is not required by the load; />Is the typical day number; />Is the number of hours per day.
Further, the second power balance constraint is expressed as:
in (1) the->The output of the thermal power unit at the t moment of the r day; />The number of the thermal power generating units; />The actual output of the wind turbine generator at the time t of the r day is shown; />The number of the wind turbine generators is the number of the wind turbine generators; /> 、/>Respectively long-term energy storage device->Discharge power; />The number of the energy storage devices is long; />Hours per day; />For day r->Load demand at time;N R for planning the number of days of the year.
Further, the coupling relationship is expressed as:
in the method, in the process of the application,respectively representing the charge and discharge power of the energy storage device at the typical day d time t long time;representing the corresponding relation between the typical day and the specific day in the planning year; />、/>Respectively long-term energy storage deviceDischarge power; />Is the typical day number; />Hours per day; LO is a technical parameter set for long-time energy storage; />Indicating that the long-term energy storage device is on a typical day +.>Final moment->A remaining charge state of the (c); />And the state of charge of the residual electric quantity of the long-time energy storage device in a specific day of the planning year is represented.
Further, the renewable energy operating constraint is expressed as:
in the method, in the process of the application,the actual output of the wind turbine generator at the time t of the r day is shown; />The maximum output limit value of the wind turbine generator at the t moment of the r day is represented; />The wind power consumption rate is the wind power consumption rate; />The number of the wind turbine generators is the number of the wind turbine generators;N R for planning the number of days of the year; />Is the number of hours per day.
Further, the thermal power generating unit operation constraint is expressed as:
in the method, in the process of the application,the output of the thermal power unit at the t moment of the r day; />Indicating the start-stop state of the thermal power generating unit g at the t moment of the r day,/day>、/>Respectively representing the minimum and maximum output limit values of the unit g; />The start-stop state of the thermal power unit g at the time k on the r day is shown; />The number of the thermal power generating units; />Hours per day;N R for planning the number of days of the year; />、/>Respectively representing the increased and decreased output of the unit g in unit time; />、/>The minimum closing time and the minimum starting time of the unit are respectively; />For rotating spare capacity coefficient +.>For day r->Load demand at time.
The second technical scheme is as follows:
a multi-time scale energy storage planning device capable of being quickly solved, which is characterized by comprising a processor and a memory for storing executable instructions of the processor; the memory is carried with the technical scheme that the multi-time-scale energy storage collaborative planning model is adopted; the processor is configured to read the executable instruction from the memory and execute the instruction to implement the first step of the technical scheme.
Compared with the prior art, the application has the following characteristics and beneficial effects:
based on the short-time energy storage, the daily power supply adequacy is guaranteed, the long-time energy storage is used as different functional positioning of seasonal electric quantity support, the long-time energy storage is discretized in the long-time and short-time energy storage collaborative planning problem, and according to the difference of energy transfer characteristics of the energy storage of different time scales, the coupling relation between the long-time and short-time energy storage is simplified, and the multi-time-scale energy storage collaborative planning model aiming at minimizing the total cost of the system is obtained.
The multi-time scale energy storage collaborative planning model is used for collaborative thermal power, wind power and multi-time scale energy storage optimization operation, and overall planning of the construction of energy storage of different time scales is achieved. The charging assembly, the discharging assembly and the energy storage assembly which take long-time energy storage into consideration can be designed in a decoupling way, and the charging power capacity, the discharging power capacity and the energy storage capacity of the energy storage assembly are independently planned and configured. Aiming at the problems of large scale and difficult solution of the model, the long-time and short-time energy storage is respectively applied to solving the power imbalance of different time scales, the short-time energy storage operation is described according to the accuracy of the hour level in a typical day, the long-time energy storage operation is described according to the accuracy of the day level in 365 days of the whole year, the complexity of the model is reduced while the energy transfer capability of the long-time energy storage season is reflected, and the solution of the model is accelerated.
Drawings
Fig. 1 is a flow chart of the present application.
Detailed Description
In the prior art, a long-time energy storage operation model and a short-time energy storage operation model are both constructed with an hour resolution. For ease of understanding, the following represents an hour-level long-term energy storage operation model with a set of constraints:
the energy balance of the long-time energy storage device at adjacent moments:
（1）
in the method, in the process of the application,is a long-term energy storage device>State of charge (SOC) of the remaining charge at time t on day r; /> 、 Respectively represent long-term energy storage device->Charging and discharging power at the time t of the r day; />、/>Respectively represent long-term energy storage device->Charging and discharging efficiency of (a); LO is a long-term energy storage technical parameter set;N T hours per day;N R to schedule the number of days of the horizontal year.
The state of charge, charge and discharge power of the long-term energy storage device is not allowed to exceed the upper limit of the planning capacity:
（2）
（3）
（4）
in the method, in the process of the application,、/>、/>respectively planning newly increased energy storage capacity, discharge power capacity and charge power capacity for long-term energy storage>、/>Respectively representing long-term energy storage device->0-1 variable of charge-discharge state at t time of day r.
The long-time energy storage device cannot be charged and discharged simultaneously:
（5）
the long-term energy storage device reaches energy balance in the balance period, i.e. the energy level is at the initial momentAnd last moment->Initial level was regressed:
（6）
wherein,is a long-term energy storage device>Is used to determine the initial charge ratio of (a).
Similarly, the hour-level short-term energy storage device also has the constraint of limiting the energy balance (adjacent time and balance period) of the short-term energy storage device at the t-th day, the state of charge, the charge-discharge power and the charge-discharge state, and the energy balance will not be described in detail herein.
The present application will be described in more detail with reference to examples.
Example 1
As shown in fig. 1, a multi-time scale energy storage planning method capable of being rapidly solved includes the following steps:
constructing a multi-time scale energy storage collaborative planning model, wherein the multi-time scale energy storage collaborative planning model comprises an objective function and a plurality of constraint conditions, wherein the objective function is used for minimizing the total cost of the system;
the constraint conditions comprise multi-time scale energy storage planning constraint, multi-time scale energy storage operation constraint, power balance constraint, renewable energy source operation constraint and thermal power generating unit operation constraint;
the multi-time scale energy storage operation constraint comprises an hour-level short-time energy storage operation constraint and a day-level long-time energy storage operation constraint;
the power balance constraint comprises a first power balance constraint in which typical intra-day-hour-level short-time energy storage and long-time energy storage participate cooperatively, a second power balance constraint in which intra-year-level long-time energy storage participates, and a coupling relation of the first power balance constraint and the second power balance constraint;
and taking a short-time energy storage operation typical scene extracted based on a clustering algorithm as input, and solving an objective function according to the constraint condition to obtain a planning scheme of the short-time energy storage component and the long-time energy storage device.
Example two
Objective function to minimize total cost of systemTo the end, the total cost of the system->Investment costs including energy storage systems of different time scales->Energy storage operation and maintenance cost->Cost of operation->Carbon emission cost->Wind abandon punishment->Expressed as:
（7）
wherein, the energy storage investment costInvestment costs including long-term energy storage and short-term energy storageThe expression is as follows:
（8）
in the method, in the process of the application,、/>planning a newly increased energy storage capacity for short-time energy storage and long-time energy storage; />Power capacity for short-term energy storage; /> 、/>Charge and discharge power capacity for long-term energy storage; />、/>、/>The unit investment cost of the energy storage capacity, the discharge power capacity and the charge power capacity is respectively; />Is the discount rate; />The full life cycle of the energy storage system; B. LO is a technical parameter set of short-time energy storage and long-time energy storage respectively.
Similarly, the cost of energy storage, operation and maintenanceBy the formulaThe expression is as follows:
（9）
in the method, in the process of the application,、/>、/>the unit operation and maintenance costs are respectively the energy storage capacity, the discharge power capacity and the charge power capacity.
Running costThe method comprises the steps of fuel cost and start-stop cost of the thermal power unit, and the fuel cost and the start-stop cost are expressed as follows:
（10）
in the method, in the process of the application, 、/> 、/>the unit fuel cost, the starting cost and the stopping cost of the g-th unit are respectively represented;the output of the machine set g at the t moment of the r day; />、/>The start-up and stop group numbers at the t moment of the r day are respectively;the number of days of planning horizontal years, the number of hours per day and the total thermal power unit number of the system are respectively.
Carbon emission costExpressed as:
（11）
in the method, in the process of the application,the carbon emission coefficient of the g-th unit; />Is the unit carbon emission cost.
Wind abandon punishmentExpressed as:
（12）
in the method, in the process of the application,punishment coefficient for wind abandon-> 、/>Respectively representing the predicted power and the actual output of the wind turbine generator set before the day at the time t of the r day; />The number of wind turbine generators in the system is the number.
Constraints of the objective function, including:
multi-time scale energy storage planning constraints;
multi-time scale energy storage operation constraints, including hour-level short-time energy storage operation constraints and day-level long-time energy storage operation constraints;
a power balance constraint;
renewable energy source operation constraints;
and (5) operating constraint of the thermal power generating unit.
The multi-time scale energy storage planning constraint is set as follows:
（13）
（14）
（15）
（16）
（17）
wherein:、/>planning a newly increased maximum power capacity and a newly increased maximum energy storage capacity for the short-time energy storage respectively; />、、/>New energy storage planning for long time respectivelyIncreased maximum charge power capacity, discharge power capacity, and energy storage capacity.
Considering that the short-time energy storage has extremely high charge and discharge efficiency, the charge and discharge difference is small in the whole electric power and electric quantity balance, the charge and discharge difference is approximately considered to be balanced within 24 hours, only the long-time energy storage solves the power imbalance of a daily time scale, the contribution of the short-time energy storage to the power imbalance on the long time scale is not considered any more, and therefore the description of the long-time energy storage is approximate to the daily time scale from an hour level, and corresponding power balance constraint is constructed. The technical effects obtained by the method are as follows: the number of decision variables describing the long-short time energy storage operation state and the long-short time energy storage operation constraint (such as the number of each operation variable of the long-time energy storage such as charge and discharge power, charge state and the like is reduced from 8760 to 365) is reduced, so that the complexity of the overall planning model is greatly reduced.
Setting a daily long-term energy storage operation constraint as follows:
（18）
（19）
（20）
（21）
（22）
（23）
in the method, in the process of the application,state of charge (SOC) for the remaining charge of the long term energy storage device on day r；/> 、/>Respectively long-term energy storage deviceDischarge power; /> 、/>Respectively long-term energy storage device->Discharge efficiency; /> 、/> 、 Planning newly increased energy storage capacity, discharge power capacity and charging power capacity for the long-time energy storage system respectively; />A 0-1 variable representing the charge and discharge state of the long-term energy storage system on day r; />Is the initial charge ratio of the long-term energy storage device.
The balance periods of the long-time and short-time energy storage are different, and the long-time energy storage can realize energy transfer in a long time range, so that the balance periods of the long-time energy storage device and the short-time energy storage device are 8760h (namely annual balance) and 24h (namely daily balance) respectively.
And dividing data by natural month days, respectively adopting a K-means clustering algorithm to plan wind power output and load data, selecting typical scenes of each month in a horizontal year, and calculating to obtain typical days of each month in wind power and load.
Setting a power balance constraint as shown in equations 24-27:
the method is characterized in that the mode of typical days of each month is selected to represent the operation process of the system planning horizontal short-term energy storage in the year, and the contribution of the short-term energy storage to the power imbalance in the day is described. Establishing an hour-level short-term power balance relation in a typical day, and ensuring the power supply adequacy in the day by utilizing short-term energy storage; and establishing a daily-grade long-term power balance relation in a planned horizontal year, and providing seasonal electric quantity support by utilizing long-term energy storage so as to solve the problem of seasonal supply and demand mismatch.
The power balance relationship within a typical day is as follows:
（24）
in the method, in the process of the application,for typical day, -> 、/> 、/> 、/>Respectively represent typical day +.>Time->Charging and discharging power of short-time energy storage and long-time energy storage, < >>The number of the short-time energy storage devices and the number of the long-time energy storage devices are respectively; />Is typical day +.>Time->Load demand of->Is the typical day number; />Representing typical day +.>Time->Actual output of the wind turbine generator;is typical day +.>Time->The output of the unit g.
The long-time energy storage solves the power imbalance of the long time sequence, and is concretely as follows:
（25）
in the method, in the process of the application,、/>respectively represent +.>Charge and discharge power of energy storage in long term>Is->Tian->Load demand at time.
The long-term energy storage typical intra-day hour scheduling condition is consistent with the corresponding natural day scheduling condition, and the SOC of the long-term energy storage typical day at the final moment is consistent with the SOC of the corresponding natural day, so that the coupling relation of two time sequences is established, and the method specifically comprises the following steps:
（26）
（27）
in the formula, a mapping is definedTo describe the correspondence of typical days to specific days within the planned horizontal year,respectively representing the charge and discharge power of the energy storage device at the typical day d time t long time,/day> 、/>Respectively charge and discharge work of the long-time energy storage device in specific days of planning yearA rate; />Indicating long-term energy storage device->On typical day +.>Final moment->SOC of (c).
Setting renewable energy operation constraints as shown in formulas 28-29:
（28）
（29）
in the method, in the process of the application,the wind power generation rate is obtained.
Setting operation constraint of the thermal power generating unit as shown in formulas 30-34:
（30）
（31）（32）
（33）/>
（34）
in the method, in the process of the application,the start-stop state of the unit g at the time t of the r day is shown; />The start-stop state of the thermal power unit g at the time k on the r day is shown; />、/>Respectively representing the minimum and maximum output limit values of the unit g; />、/>Respectively representing the increased and decreased output of the unit g in unit time, namely the ascending/descending climbing rate of the unit; />、/>The minimum closing time and the minimum starting time of the unit are respectively; />For rotating spare capacity coefficients.
The constraints are respectively output constraint, climbing rate constraint, minimum shutdown/startup time constraint and rotation standby constraint of the thermal power generating unit.
Example III
And the objective function and the constraint conditions are synthesized to form a mixed integer linear programming model for multi-time scale energy storage collaborative programming, and the solving efficiency is greatly improved while the accuracy is considered. And taking a short-time energy storage operation typical scene extracted based on a clustering algorithm as input, and solving by adopting a single-target optimization algorithm to obtain a planning scheme of the short-time energy storage component and the long-time energy storage device. Specifically, a solver (e.g., cplex) is utilized to solve, and the resulting planning scheme includes: the short-time energy storage component plans newly increased energy storage capacity, power capacity, and the long-time energy storage device plans newly increased charging power capacity, discharging power capacity and energy storage capacity.
It should be noted that, the above-mentioned multi-time-scale energy storage planning device capable of being quickly solved is further used for implementing the method steps corresponding to each embodiment in the multi-time-scale energy storage planning method capable of being quickly solved as shown in fig. 1, and the description of the present application is not repeated here.
It should be noted that, in each embodiment of the present application, each functional unit/module may be integrated in one processing unit/module, or each unit/module may exist alone physically, or two or more units/modules may be integrated in one unit/module. The integrated units/modules described above may be implemented either in hardware or in software functional units/modules.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments described herein may be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For a hardware implementation, the processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the flow of an embodiment may be accomplished by a computer program to instruct the associated hardware. When implemented, the above-described programs may be stored in or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. The computer readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the scope of the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.
Claims (2)
1. The multi-time scale energy storage planning method capable of being solved rapidly is characterized by comprising the following steps of:
constructing a multi-time scale energy storage collaborative planning model, wherein the multi-time scale energy storage collaborative planning model comprises an objective function and a plurality of constraint conditions, wherein the objective function is used for minimizing the total cost of the system;
the total cost of the system includes investment costEnergy storage operation and maintenance cost->Cost of operation->Cost of carbon emissionWind abandon punishment->；
Wherein, the energy storage investment costEnergy storage operation and maintenance cost->Expressed as:
in the method, in the process of the application,、/>planning a newly increased energy storage capacity for short-time energy storage and long-time energy storage; />Power capacity for short-term energy storage; />、/>Charge and discharge power capacity for long-term energy storage; />、/>The unit investment cost of the energy storage capacity, the discharge power capacity and the charge power capacity is respectively; />Is the discount rate; />The full life cycle of the energy storage system; B. LO is a technical parameter set of short-time energy storage and long-time energy storage respectively; />、/>、/>The unit operation and maintenance cost is respectively the energy storage capacity, the discharge power capacity and the charge power capacity;
the constraint conditions comprise multi-time scale energy storage planning constraint, multi-time scale energy storage operation constraint, power balance constraint, renewable energy source operation constraint and thermal power generating unit operation constraint;
the multi-time scale energy storage planning constraint comprises: setting a value range of the energy storage power and the energy storage capacity newly added in the short-time energy storage planning; setting a charging power capacity, a discharging power capacity and a value range of an energy storage capacity which are newly added in a long-time energy storage planning;
the multi-time scale energy storage operation constraint comprises an hour-level short-time energy storage operation constraint and a day-level long-time energy storage operation constraint;
the daily long-term energy storage operation constraint is expressed as:
in the method, in the process of the application,the charge state of the residual electric quantity of the long-time energy storage device on the r day; />、/>Respectively long-term energy storage deviceDischarge power; />、/>Respectively charging and discharging efficiency of the long-time energy storage device; LO is a technical parameter set for long-time energy storage;N R for planning the number of days of the year; />、/>、/>Respectively planning newly increased energy storage capacity and discharge work for long-term energy storage systemRate capacity, charge power capacity; />Representing the charge and discharge state of the long-term energy storage device on the r day;the initial electric quantity proportion of the long-time energy storage device;
the power balance constraint comprises a first power balance constraint in which typical intra-day-hour-level short-time energy storage and long-time energy storage participate cooperatively, a second power balance constraint in which intra-year-level long-time energy storage participates, and a coupling relation of the first power balance constraint and the second power balance constraint;
the first power balance constraint is expressed as:
in the method, in the process of the application,is a typical day; />Is typical day +.>Time->The output of the thermal power generating unit; />The number of the thermal power generating units; />Representing typical day +.>Time->Actual output of the wind turbine generator; />The number of the wind turbine generators is the number of the wind turbine generators; /> 、/>Respectively represent typical day +.>Time->Charging and discharging power of the short-time energy storage device; /> 、/>Respectively representing the charge and discharge power of the energy storage device at the typical day d time t long time; />The number of the short-time energy storage devices and the number of the long-time energy storage devices are respectively; />Is typical day +.>Time->Is not required by the load; />Is the typical day number; />Hours per day;
the second power balance constraint is expressed as:
in the method, in the process of the application,the output of the thermal power unit at the t moment of the r day; />The number of the thermal power generating units; />The actual output of the wind turbine generator at the time t of the r day is shown; />The number of the wind turbine generators is the number of the wind turbine generators; /> 、/>Respectively long-term energy storage device->Discharge power; />The number of the energy storage devices is long; />Hours per day; />For day r->Load demand at time;N R for planning the number of days of the year;
the coupling relation is expressed as:
in the method, in the process of the application,respectively representing the charge and discharge power of the energy storage device at the typical day d time t long time; />Representing the corresponding relation between the typical day and the specific day in the planning year; />、/>Respectively charging and discharging power of the long-time energy storage device in a specific day of a planning year; />Is the typical day number; />Hours per day; LO is a technical parameter set for long-time energy storage; />Indicating long-term energy storage deviceIs placed at the typical day->Final moment->A remaining charge state of the (c); />Representing the state of charge of the residual electric quantity of the long-time energy storage device at a specific day in a planning year;
the renewable energy operation constraint is expressed as:
in the method, in the process of the application,the actual output of the wind turbine generator at the time t of the r day is shown; />The maximum output limit value of the wind turbine generator at the t moment of the r day is represented; />The wind power consumption rate is the wind power consumption rate; />The number of the wind turbine generators is the number of the wind turbine generators;N R for planning the number of days of the year; />Hours per day;
the thermal power generating unit operation constraint is expressed as:
in the method, in the process of the application,the output of the thermal power unit at the t moment of the r day; />Indicating the start-stop state of the thermal power generating unit g at the t moment of the r day,/day>、/>Respectively representing the minimum and maximum output limit values of the unit g; />The start-stop state of the thermal power unit g at the time k on the r day is shown; />The number of the thermal power generating units; />Hours per day;N R for planning the number of days of the year; />、/>Respectively representing the increased and decreased output of the unit g in unit time; />、/>The minimum closing time and the minimum starting time of the unit are respectively; />For rotating spare capacity coefficient +.>Load demand at time t on day r;
and taking a short-time energy storage operation typical scene extracted based on a clustering algorithm as input, and solving an objective function according to the constraint condition to obtain a planning scheme of the short-time energy storage component and the long-time energy storage device.
2. A multi-time scale energy storage planning device capable of being quickly solved, which is characterized by comprising a processor and a memory for storing executable instructions of the processor; the memory is carried with a multi-time scale energy storage collaborative planning model;
the multi-time scale energy storage collaborative planning model comprises an objective function and a plurality of constraint conditions, wherein the objective function is used for minimizing the total cost of the system;
the total cost of the system includes investment costEnergy storage operation and maintenance cost->Cost of operation->Cost of carbon emissionWind abandon punishment->；
Wherein, the energy storage investment costEnergy storage operation and maintenance cost->Expressed as:
in the method, in the process of the application,、/>planning a newly increased energy storage capacity for short-time energy storage and long-time energy storage; />Power capacity for short-term energy storage; />、/>Charge and discharge power capacity for long-term energy storage; />、/>The unit investment cost of the energy storage capacity, the discharge power capacity and the charge power capacity is respectively; />Is the discount rate; />The full life cycle of the energy storage system; B. LO is a technical parameter set of short-time energy storage and long-time energy storage respectively; />、/>、/>The unit operation and maintenance cost is respectively the energy storage capacity, the discharge power capacity and the charge power capacity;
the constraint conditions comprise multi-time scale energy storage planning constraint, multi-time scale energy storage operation constraint, power balance constraint, renewable energy source operation constraint and thermal power generating unit operation constraint;
the multi-time scale energy storage planning constraint comprises: setting a value range of the energy storage power and the energy storage capacity newly added in the short-time energy storage planning; setting a charging power capacity, a discharging power capacity and a value range of an energy storage capacity which are newly added in a long-time energy storage planning;
the multi-time scale energy storage operation constraint comprises an hour-level short-time energy storage operation constraint and a day-level long-time energy storage operation constraint;
the daily long-term energy storage operation constraint is expressed as:
in the method, in the process of the application,the charge state of the residual electric quantity of the long-time energy storage device on the r day; />、/>Respectively long-term energy storage deviceDischarge power; />、/>Respectively charging and discharging efficiency of the long-time energy storage device; LO is a technical parameter set for long-time energy storage;N R for planning the number of days of the year; />、/>、/>Planning newly increased energy storage capacity, discharge power capacity and charging power capacity for the long-time energy storage system respectively; />Representing the charge and discharge state of the long-term energy storage device on the r day;the initial electric quantity proportion of the long-time energy storage device;
the power balance constraint comprises a first power balance constraint in which typical intra-day-hour-level short-time energy storage and long-time energy storage participate cooperatively, a second power balance constraint in which intra-year-level long-time energy storage participates, and a coupling relation of the first power balance constraint and the second power balance constraint;
the first power balance constraint is expressed as:
in the method, in the process of the application,is a typical day; />Is typical day +.>Time->The output of the thermal power generating unit; />The number of the thermal power generating units; />Representing typical day +.>Time->Actual output of the wind turbine generator; />The number of the wind turbine generators is the number of the wind turbine generators; /> 、/>Respectively represent typical day +.>Time->Charging and discharging power of the short-time energy storage device; /> 、/>Respectively representing the charge and discharge power of the energy storage device at the typical day d time t long time; />The number of the short-time energy storage devices and the number of the long-time energy storage devices are respectively; />Is typical day +.>Time->Is not required by the load; />Is the typical day number; />Hours per day;
the second power balance constraint is expressed as:
in the method, in the process of the application,the output of the thermal power unit at the t moment of the r day; />The number of the thermal power generating units; />The actual output of the wind turbine generator at the time t of the r day is shown; />The number of the wind turbine generators is the number of the wind turbine generators; /> 、/>Respectively long-term energy storage device->Discharge power; />The number of the energy storage devices is long; />Hours per day; />For day r->Load demand at time;N R for planning the number of days of the year;
the coupling relation is expressed as:
in the method, in the process of the application,respectively representing the charge and discharge power of the energy storage device at the typical day d time t long time; />Representing the corresponding relation between the typical day and the specific day in the planning year; />、/>Respectively charging and discharging power of the long-time energy storage device in a specific day of a planning year; />Is the typical day number; />Hours per day; LO is a technical parameter set for long-time energy storage; />Indicating that the long-term energy storage device is on a typical day +.>Final moment->A remaining charge state of the (c); />Representing the state of charge of the residual electric quantity of the long-time energy storage device at a specific day in a planning year;
the renewable energy operation constraint is expressed as:
in the method, in the process of the application,the actual output of the wind turbine generator at the time t of the r day is shown; />The maximum output limit value of the wind turbine generator at the t moment of the r day is represented; />The wind power consumption rate is the wind power consumption rate; />The number of the wind turbine generators is the number of the wind turbine generators;N R for planning the number of days of the year; />Hours per day;
the thermal power generating unit operation constraint is expressed as:
in the method, in the process of the application,the output of the thermal power unit at the t moment of the r day; />Indicating the start-stop state of the thermal power generating unit g at the t moment of the r day,/day>、/>Respectively representing the minimum and maximum output limit values of the unit g; />The start-stop state of the thermal power unit g at the time k on the r day is shown; />The number of the thermal power generating units; />Hours per day;N R for planning the number of days of the year; />、/>Respectively representing the increased and decreased output of the unit g in unit time; />、/>The minimum closing time and the minimum starting time of the unit are respectively; />For rotating spare capacity coefficient +.>Load demand at time t on day r;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the steps of: and taking a short-time energy storage operation typical scene extracted based on a clustering algorithm as input, and solving an objective function according to the constraint condition to obtain a planning scheme of the short-time energy storage component and the long-time energy storage device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311071651.5A CN116822908B (en) | 2023-08-24 | 2023-08-24 | Multi-time-scale energy storage planning method and equipment capable of being rapidly solved |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311071651.5A CN116822908B (en) | 2023-08-24 | 2023-08-24 | Multi-time-scale energy storage planning method and equipment capable of being rapidly solved |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116822908A CN116822908A (en) | 2023-09-29 |
| CN116822908B true CN116822908B (en) | 2023-11-17 |
Family
ID=88113041
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311071651.5A Active CN116822908B (en) | 2023-08-24 | 2023-08-24 | Multi-time-scale energy storage planning method and equipment capable of being rapidly solved |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116822908B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110071505A (en) * | 2019-06-04 | 2019-07-30 | 清华大学 | The power transmission network enlarging of the access containing large-scale wind power configures joint planing method with energy storage |
| CN114301064A (en) * | 2021-12-30 | 2022-04-08 | 国网河南省电力公司经济技术研究院 | Distributed power supply absorption capacity improving method based on net rack flexibility and energy storage access |
| CN114417625A (en) * | 2022-01-24 | 2022-04-29 | 太原理工大学 | Seasonal energy storage solution considering wind-gas complementary characteristic |
| CN114818078A (en) * | 2022-05-09 | 2022-07-29 | 东南大学 | Planning method for net zero energy consumption building energy system considering long-term and short-term uncertainty |
| CN115983589A (en) * | 2023-01-03 | 2023-04-18 | 国网重庆市电力公司经济技术研究院 | Multi-type power supply time sequence planning method and system under long-time scale |
| CN116050865A (en) * | 2023-01-03 | 2023-05-02 | 国网安徽省电力有限公司经济技术研究院 | Planning method for hydrogen energy storage power station under seasonal time scale |
| CN116128315A (en) * | 2022-12-02 | 2023-05-16 | 上海交通大学 | Long-short term energy storage planning method, system, medium and equipment |
| CN116562554A (en) * | 2023-04-27 | 2023-08-08 | 福州大学 | Long-short-period energy storage joint planning method considering flexible supply-demand balance |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11878602B2 (en) * | 2019-11-22 | 2024-01-23 | State Grid Fujian Electric Power Co., Ltd. | Method for establishing active distribution network planning model considering location and capacity determination of electric vehicle charging station |
-
2023
- 2023-08-24 CN CN202311071651.5A patent/CN116822908B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110071505A (en) * | 2019-06-04 | 2019-07-30 | 清华大学 | The power transmission network enlarging of the access containing large-scale wind power configures joint planing method with energy storage |
| CN114301064A (en) * | 2021-12-30 | 2022-04-08 | 国网河南省电力公司经济技术研究院 | Distributed power supply absorption capacity improving method based on net rack flexibility and energy storage access |
| CN114417625A (en) * | 2022-01-24 | 2022-04-29 | 太原理工大学 | Seasonal energy storage solution considering wind-gas complementary characteristic |
| CN114818078A (en) * | 2022-05-09 | 2022-07-29 | 东南大学 | Planning method for net zero energy consumption building energy system considering long-term and short-term uncertainty |
| CN116128315A (en) * | 2022-12-02 | 2023-05-16 | 上海交通大学 | Long-short term energy storage planning method, system, medium and equipment |
| CN115983589A (en) * | 2023-01-03 | 2023-04-18 | 国网重庆市电力公司经济技术研究院 | Multi-type power supply time sequence planning method and system under long-time scale |
| CN116050865A (en) * | 2023-01-03 | 2023-05-02 | 国网安徽省电力有限公司经济技术研究院 | Planning method for hydrogen energy storage power station under seasonal time scale |
| CN116562554A (en) * | 2023-04-27 | 2023-08-08 | 福州大学 | Long-short-period energy storage joint planning method considering flexible supply-demand balance |
Non-Patent Citations (2)
| Title |
|---|
| 大规模新能源并网系统电源规划方法;王建学;李清涛;王秀丽;黄启航;刘树桦;钱涛;曹晓宇;;中国电机工程学报(第10期);第64-74页 * |
| 考虑多类型能源转换与存储的综合能源微网优化运行研究;刘敦楠;秦光宇;李奇;;南方电网技术(第03期);第111-121页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116822908A (en) | 2023-09-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| CN102694391B (en) | Day-ahead optimal scheduling method for wind-solar storage integrated power generation system | |
| CN107732949B (en) | Energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round | |
| CN106485352A (en) | A kind of multiclass power supply generation schedule combination decision-making method | |
| CN111404206B (en) | Wind-solar energy storage power generation system capacity double-layer planning method considering investment return constraint | |
| CN111009895B (en) | Microgrid optimal scheduling method, system and equipment | |
| CN114037191A (en) | Virtual power plant optimal scheduling method, device, equipment and medium based on big data | |
| Georgiou et al. | A genetic algorithm driven linear programming for battery optimal scheduling in nearly zero energy buildings | |
| Zhang et al. | Optimized scheduling model for isolated microgrid of wind-photovoltaic-thermal-energy storage system with demand response | |
| CN111523947B (en) | Virtual power plant power generation cost generation method | |
| CN111932025B (en) | Comprehensive energy system construction multi-stage planning method considering photovoltaic randomness | |
| CN111898801B (en) | Method and system for configuring multi-energy complementary power supply system | |
| CN106339773B (en) | Sensitivity-based constant volume planning method for distributed power supply of active power distribution network | |
| CN116822908B (en) | Multi-time-scale energy storage planning method and equipment capable of being rapidly solved | |
| CN117114281A (en) | Determination method of flexible resource multi-stage planning scheme | |
| CN116029114A (en) | Comprehensive energy base optimal configuration method based on annual time sequence production simulation | |
| CN108683211B (en) | Virtual power plant combination optimization method and model considering distributed power supply volatility | |
| CN115983589A (en) | Multi-type power supply time sequence planning method and system under long-time scale | |
| CN114844119A (en) | Energy storage power generation installation and capacity optimal configuration method and system | |
| CN114358430A (en) | Novel rural power distribution network planning strategy based on robust optimization | |
| CN114465226A (en) | Method for establishing multi-level standby acquisition joint optimization model of power system | |
| CN111563699A (en) | Power system distribution robust real-time scheduling method and system considering flexibility requirement | |
| CN112510702B (en) | Comprehensive energy station planning method and system for renewable energy power generation and gas production | |
| CN116914860B (en) | Power supply capacity configuration optimization method and system for multi-energy complementary power generation system | |
| CN112491072B (en) | Energy storage layout method and device for multi-terminal hybrid direct current transmission system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |