CN107732949B - Energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round - Google Patents
Energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round Download PDFInfo
- Publication number
- CN107732949B CN107732949B CN201710974712.7A CN201710974712A CN107732949B CN 107732949 B CN107732949 B CN 107732949B CN 201710974712 A CN201710974712 A CN 201710974712A CN 107732949 B CN107732949 B CN 107732949B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- power
- cost
- storage
- parameters
- 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 265
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000004088 simulation Methods 0.000 claims abstract description 31
- 230000005540 biological transmission Effects 0.000 claims description 13
- 230000009194 climbing Effects 0.000 claims description 9
- 238000007600 charging Methods 0.000 claims description 8
- 238000007599 discharging Methods 0.000 claims description 7
- 230000008901 benefit Effects 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000010977 unit operation Methods 0.000 claims description 6
- 238000012935 Averaging Methods 0.000 claims description 5
- 238000005086 pumping Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 230000010485 coping Effects 0.000 claims description 2
- 238000000691 measurement method Methods 0.000 claims 1
- 230000001932 seasonal effect Effects 0.000 abstract description 4
- 230000005611 electricity Effects 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 abstract description 2
- 230000002349 favourable effect Effects 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 description 9
- 238000011161 development Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000006872 improvement Effects 0.000 description 3
- 230000001737 promoting effect Effects 0.000 description 3
- 238000009987 spinning Methods 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 3
- 230000033228 biological regulation Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 229940079593 drug Drugs 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000004870 electrical engineering Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
-
- H02J3/386—
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses an energy storage arrangement point constant volume method integrating wind power all-year-round multi-season characteristics, which comprises the following steps of: acquiring power grid parameters and energy storage parameters, and establishing an energy storage planning model based on operation simulation by taking the minimum sum of operation cost, energy storage investment, wind abandonment punishment and delivery cost as a target function; positions with large battery energy storage requirements are identified through multi-scene analysis to serve as battery energy storage alternative nodes, and two indexes of energy storage requirement ratio and equivalent annual maximum energy storage requirement day are provided to synthesize the difference of energy storage requirements in different seasons, so that a final stationing and constant volume scheme is determined. The invention considers the investment cost of energy storage in the planning target, simultaneously comprehensively considers the seasonal difference of energy storage requirements in different seasons by utilizing the energy storage requirement ratio, effectively relieves the problem of wind abandon and electricity limiting caused by insufficient adjusting capability on the basis of ensuring the economical efficiency, is favorable for global consideration, and improves the overall economical efficiency and utilization efficiency of energy storage planning.
Description
Technical Field
The invention belongs to the field of electrical engineering, and particularly relates to an energy storage, distribution and volume fixing method integrating multi-season characteristics of wind power all the year around.
Background
Since the 21 st century, under the dual pressure of energy crisis and environmental pollution, wind power has the advantages of scale development conditions, good commercial development prospect and the like due to mature technology and is developed rapidly. In 2016, 2337 thousands kilowatts are newly added in China, the accumulated installed capacity reaches 1.69 hundred million kilowatts, and the installed capacity is twice of that of the second U.S. in the world. However, due to the randomness and the volatility of wind power output, large-scale wind power grid connection brings great challenges to a power system. Especially, the shortage of the adjustable capacity of the system can cause serious wind abandon and obstruct the healthy development of wind power. The rapid development of the energy storage technology provides a good solution for dealing with wind power consumption. However, at the present stage, the development of energy storage is not mature enough, and the problems of high investment cost, poor economy and the like exist. Therefore, an economical and effective energy storage planning method is needed to be adopted, so that the problem of wind curtailment is relieved, and the planning economy is guaranteed. At present, in China, a common energy storage planning method comprises energy storage planning based on fluctuation stabilization and minimum energy storage planning of full-consumption wind power, wherein the energy storage planning only considers the effect of energy storage in a smooth wind power curve but neglects the contribution of energy storage in promoting wind power consumption, so that the wind curtailment rate under high-consumption energy storage investment is still high, and the minimum energy storage planning takes full-consumption wind power as a target but does not consider the high cost of energy storage, so that the investment is low in efficiency. Meanwhile, the energy storage planning under the single-load day is only considered by the energy storage planning and the seasonal difference of the energy storage demand is not considered, so that the energy storage investment layout is further unreasonable.
Therefore, the technical problems of poor economy and incapability of effectively relieving wind abandon and electricity limiting caused by insufficient adjusting capacity exist in the prior art.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides an energy storage, distribution and constant volume method integrating the multi-season characteristics of wind power all the year around, so that the technical problems of poor economy and incapability of effectively relieving wind abandon and electricity limitation caused by insufficient regulation capacity in the prior art are solved.
In order to achieve the purpose, the invention provides an energy storage, distribution and constant volume method integrating the annual multi-season characteristics of wind power, which comprises the following steps:
(1) acquiring power grid parameters and energy storage parameters, and establishing an energy storage planning model based on operation simulation by taking the minimum sum of operation cost, energy storage investment, wind abandon punishment and delivery cost as a target function and taking constraint of a battery energy storage system, constraint of a pumped storage system, constraint of thermal power generating unit operation, constraint of delivery line operation and constraint of an operation simulation system as constraint conditions;
(2) constructing a solving model according to the power grid parameters, the energy storage parameters and an energy storage planning model based on operation simulation;
(3) changing typical days, battery energy storage cost and wind abandon penalty coefficients in sequence to obtain a series of scenes, averaging the battery energy storage values under each scene of each node, and obtaining the top N with the maximum average valueoEach node is a standby node for storing energy by a battery, and the standby node for pumping water and storing energy is a construction position;
(4) obtaining energy storage investment of a typical day in winter and energy storage investment of a typical day in summer by using a solution model, wherein the quotient of the energy storage investment of the typical day in winter and the energy storage investment of the typical day in summer is an energy storage requirement ratio, the typical day with larger energy storage investment is a maximum energy storage requirement day, and the energy storage requirement ratio is used for obtaining an equivalent maximum energy storage requirement day of the whole year;
(5) and updating the energy storage planning model based on the operation simulation by utilizing the annual equivalent maximum energy storage requirement day to obtain the updated battery energy storage cost and the pumped storage cost, and obtaining the final point distribution constant volume scheme according to the alternative node of the battery energy storage, the alternative node of the pumped storage energy, the updated battery energy storage cost and the pumped storage cost based on the solution model.
Further, the grid parameters include: economic parameters of thermal power generating units, economic parameters of wind power generating units, economic parameters of power transmission among regions, technical parameters of systems and thermal power, and power data P of wind power and loads in typical days of winter and summerW，i，t，
The technical parameters of the system comprise: line-node association matrix A, line admittance blRated operating power of outgoing line
Line transport capacity Fl maxThe system reserves a rotation standby ratio R for coping with the unit fault;
the thermal power technical parameters comprise: total number N of thermal power generating unitsgUpper and lower limits P of output of each thermal power generating unit ii max、Pi minUpward and downward climbing rate
Maximum start and stop power
Minimum continuous start-up and shut-down time
Initial running state and initial running time of unit
Ti 0；
The economic parameter of the thermal power generating unit is an operation cost secondary curve parameter ai，bi，ciThe wind turbine generator economic parameter is a wind abandon penalty coefficient kwThe inter-regional power transmission economic parameter is a line delivery income coefficient ko。
Further, the energy storage parameters include technical parameters and economic parameters of battery energy storage, technical parameters and economic parameters of pumped storage, and the technical parameters and economic parameters of battery energy storage include: number N of alternative nodes for battery energy storage planningsUnit power cost, unit energy cost of battery energy storage
Charge-discharge efficiency of battery energy storage
Rated unit power of battery energy storageAccording to the maximum continuous charging and discharging time H of rated power, the upper limit of the planned quantity of the battery energy storage units of each node
The technical parameters and economic parameters of pumped storage include: number of candidate nodes N for pumped storage planningpCost per unit power, cost per unit energy of pumped storage
Charge and discharge efficiency of pumped storage
Rated power of pumped storage unit
Upper limit of planned quantity of pumped storage units of each node
Further, the objective function is:
minΨ＝Cgen+Cstr+Cwin-Cout，
wherein, CgenRepresents the running cost, CstrRepresents the energy storage investment, CwinDenotes a wind curtailment penalty, CoutIndicates the benefit of the outgoing line, -CoutThe cost of the delivery is expressed in terms of,
wherein T is the total time period number of the operation simulation, Np，Ns，NcThe total number of the pumped storage power station, the battery storage power station and the thermal power generating unit,
respectively representing the output and the running cost of the unit at the time period t of the i time period; SPt wThe total amount of the abandoned wind in the t-th time period; plnk，tThe delivered power of the t-th time period;
respectively planning the power capacity and the energy capacity of the mth battery energy storage power station; eEPS，iEnergy capacity, Q, for the ith pumped storage power stationPS，iThe number of the pumped storage groups of the ith pumped storage power station,
for the rated power, k, of pumped storage unitsoExpressing the coefficient of delivery revenue, delta the discount rate, YRsRepresenting the battery energy storage full life cycle, YRpIndicating pumped storage life cycle, NdIndicating annual energy storage, etcThe number of days of effective utilization is,
unit power cost, energy cost for battery energy storage
The battery energy storage cost to date is converted according to the whole life cycle,
unit power cost and energy cost of pumped storage respectivelyAnd converting the pumped storage cost to the daily cost according to the whole life cycle of the pump storage system.
Further, the battery energy storage system constraints include: the method comprises the following steps of battery energy storage system operation constraint, battery energy storage system discharge and charge power constraint, battery energy storage system real-time energy constraint, battery energy storage system planning constraint, battery energy storage system energy capacity and power capacity equality constraint, and battery energy storage single-node energy storage unit number upper limit constraint;
the pumped-hydro energy storage system constraints include: the operation of the pumped storage system is restricted, the discharge and charge power of the pumped storage system is restricted, the charge and discharge state of the pumped storage system is restricted, and the operation energy of the pumped storage system is restricted; planning constraint of a pumped storage system and energy capacity constraint of a pumped storage power station;
the thermal power generating unit operation constraint comprises: the method comprises the following steps of thermal power unit operation constraint, thermal power unit output upper and lower limit constraint, thermal power unit minimum continuous start-stop time constraint, thermal power unit climbing upper limit constraint and thermal power unit maximum start-stop power constraint;
the operational simulation system constraints include: node power balance constraint, rotation standby constraint and line power flow constraint.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the method considers the difference of the energy storage requirements of the same node in different seasons, establishes a model based on an operation simulation method, considers the investment cost of energy storage in an energy storage planning model based on the operation simulation, and ensures the economy of investment while promoting wind power consumption by taking the minimum sum of the operation cost, the energy storage investment, the wind abandonment penalty and the delivery cost as a target function. And meanwhile, identifying a node with a large battery energy storage demand as a battery energy storage alternative node through multi-scene analysis, and synthesizing an energy storage stationing constant volume scheme in different seasons by calculating an energy storage demand ratio and two indexes of a full-year equivalent maximum energy storage demand day. The invention is both effective and economical.
(2) The invention can effectively relieve the problem of wind abandon caused by insufficient regulation capacity through reasonable energy storage configuration; the economic cost of energy storage is considered in planning, the operation cost and the planning cost are put into the same objective function, the wind power consumption is promoted, the excessive investment of the energy storage is avoided, and meanwhile, the setting of a wind abandoning punishment coefficient allows a decision maker to perform preference adjustment between the wind power consumption and the economy of a unit; the energy storage requirements on the day time scale are considered, seasonal differences of energy storage distribution points and constant volume schemes are integrated, and the overall economy of energy storage planning is further improved; an energy storage planning model which is complete and easy to solve and based on operation simulation is established, the energy storage planning model comprises power type battery energy storage and energy type pumped storage, and the models are subjected to linear transformation, so that the rapid solution is facilitated.
Drawings
Fig. 1 is a flow chart of an energy storage, distribution and volume fixing method for integrating multi-seasonal characteristics of wind power all the year round, which is provided by the embodiment of the invention;
fig. 2(a) is a typical winter daily load prediction curve provided in examples 1 and 2 of the present invention;
fig. 2(b) is a typical winter solar wind power prediction curve provided in embodiment 1 and embodiment 2 of the present invention;
FIG. 3(a) is a typical daily load prediction curve in summer provided in examples 1 and 2 of the present invention;
fig. 3(b) is a typical summer solar wind power prediction curve provided in embodiment 1 and embodiment 2 of the present invention;
fig. 4 is a curve of output and wind curtailment of various units of the system before energy storage configuration provided in embodiment 1 of the present invention;
fig. 5 is a curve of output and wind curtailment of various units of the system after energy storage configuration provided in embodiment 1 of the present invention;
fig. 6 is a curve of output and wind curtailment of various units of the system before energy storage configuration provided in embodiment 2 of the present invention;
fig. 7 is a graph of output and wind curtailment of various units of the system after energy storage configuration according to embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, an energy storage, distribution and volume fixing method for integrating multi-season characteristics of wind power all year round comprises the following steps:
step 1: and acquiring power grid parameters and energy storage parameters.
The grid parameters include: economic parameters of thermal power generating units, economic parameters of wind power generating units, economic parameters of power transmission among regions, technical parameters of systems and thermal power, and power data P of wind power and loads in typical days of winter and summerW，i，t，
The technical parameters of the system comprise: line-node association matrix A, line admittance blRated operating power of outgoing line
Line transport capacity Fl maxThe system reserves a rotational reserve ratio R for handling the unit fault.
The thermal power technical parameters comprise: total number N of thermal power generating unitsgUpper and lower limits P of output of each thermal power generating unit ii max、Pi minUpward and downward climbing rate
Maximum start and stop powerMinimum continuous start-up and shut-down time
Initial running state and initial running time of unit
Ti 0。
The economic parameter of the thermal power generating unit is an operation cost secondary curve parameter ai，bi，ciThe wind turbine generator economic parameter is a wind abandon penalty coefficient kwThe inter-regional power transmission economic parameter is a line delivery income coefficient ko。
The energy storage parameters comprise technical parameters and economic parameters of battery energy storage, technical parameters and economic parameters of pumped storage, and the technical parameters and economic parameters of battery energy storage comprise: number N of alternative nodes for battery energy storage planningsUnit power cost, unit energy cost of battery energy storage
Charge-discharge efficiency of battery energy storage
Rated power for battery energy storage
According to the maximum continuous charging and discharging time H of rated power, the upper limit of the planned quantity of the battery energy storage units of each node
The technical parameters and economic parameters of pumped storage include: number of candidate nodes N for pumped storage planningpCost per unit power, cost per unit energy of pumped storage
Charge and discharge efficiency of pumped storage
Rated power of pumped storage unit
Upper limit of planned quantity of pumped storage units of each node
Step 2: establishing energy storage planning model based on operation simulation
An objective function: min Ψ ═ Cgen+Cstr+Cwin-CoutWherein, CgenRepresents the running cost, CstrRepresents the energy storage investment, CwinDenotes a wind curtailment penalty, coutIndicates the benefit of the outgoing line, -CoutThe method has the advantages that the delivery cost is expressed, and on the premise of ensuring the utilization rate of delivery lines, the following four aspects are reflected in the improvement of the planning target of a large-scale wind power grid-connected power system on the consumption of renewable energy sources: firstly, optimizing and arranging a conventional unit of the system to reduce the operation cost, and secondly, reducing the investment cost of the energy storage system as much as possible; thirdly, wind power generation resources are fully utilized, and wind abandon punishment is reduced as much as possible; fourthly, renewable energy is fully utilized to send out the line, the line utilization rate is improved, and the outward sending line income is increased. The detailed expressions of the terms are as follows:
where T is the total number of time periods for the running simulation, Np，Ns，NcThe total number of the pumped storage power station, the battery storage power station and the thermal power generating unit。
Respectively representing the output and the running cost of the unit at the time period t of the i time period; SPt wThe total amount of the abandoned wind in the t-th time period; plnk，tThe delivered power of the t-th time period;
respectively planning the power capacity and the energy capacity of the mth battery energy storage power station; eEPSiEnergy capacity, Q, for the ith pumped storage power stationPS，iThe number of the pumped storage groups of the ith pumped storage power station,
the rated power of the pumped storage unit. k is a radical ofoExpressing the coefficient of delivery revenue, delta the discount rate, YRsRepresenting the battery energy storage full life cycle, YRpIndicating pumped storage life cycle, NdThe number of days for equivalent utilization of energy storage in the whole year is shown,
unit power cost, energy cost for battery energy storage
The battery energy storage cost to date is converted according to the whole life cycle,
unit power cost and energy cost of pumped storage respectively
And converting the pumped storage cost to the daily cost according to the whole life cycle of the pump storage system.
The constraints are as follows:
(1) battery energy storage system restraint
The constraints (2) - (3) are the operation constraints of the battery energy storage system, (2) are the discharge and charge power constraints of the battery energy storage system, (3) are the real-time energy constraints of the battery energy storage system, and (4) - (6) are the battery energy storageEnergy system planning constraint, (4) (5) is battery energy storage system energy capacity and power capacity equality constraint respectively, (6) is battery energy storage single-node energy storage unit number upper limit constraint, (7) - (10) is nonlinear variable linearization processing process, (7) represents integer variable of planning unit number by binary method, (8) represents left type linearization by introducing intermediate variable, (9) (10) constrains intermediate variable by large M method, M is a constant which is ensured to be larger than the intermediate variable, and (11) represents 0-1 variable in model. Wherein the content of the first and second substances,
the discharge power and the charge power of the node m in the time period t are respectively; eBS，m(t)，EBS，m(t-1) represents the energy of the battery energy storage power station in the t and t-1 th time periods, respectively, EBS，m(T)，
Respectively representing the energy of the last period of the running simulation and the initial energy before the running simulation.
A variable 0-1 representing the charge-discharge state, wherein 0 represents charge and 1 represents discharge;
the charge-discharge efficiency for storing energy for the battery; em0Represents the initial value of the energy stored in the node m and QBS，mThe number of energy storage units for node m,the maximum allowed number of energy storage units is the node m;the rated unit power of the battery energy storage of the node m is represented, and H is the maximum continuous charging and discharging hours of the battery energy storage power station; x is the number ofm，kIs a 0-1 variable that matches the corresponding binary value, k 1, 2m；
Is to represent a token binary 0-1 variable xm，kAnd charge-discharge 0-1 variable
The intermediate variable of the product.
(2) Pumped storage system restraint
The constraints (12) to (15) are the operation constraints of the pumped storage system, the constraints (12) and (13) are respectively the discharge power and the charging power constraints of the pumped storage system, and the pumped storage unit is charged at constant power for economyThe charge-discharge state constraint of the pumped storage system is shown in the step (14), and the operation energy constraint of the pumped storage system is shown in the step (15); constraints (16) - (17) are pumped storage system planning constraints, (16) characterize pumped storage unit quantity equation constraints, (17) are pumped storage power station energy capacity constraints, and (18) represent 0-1 variables in the model. Wherein the content of the first and second substances,respectively the discharging power and the charging power of the unit k time period t of the power station i;
respectively the discharge and charge states of the unit of the power station i at the time period k t, βPS，i，k1 represents a variable 0-1 of the planning state of a unit k of a power station i, and participates in planning for the unit, otherwise does not participate in planning; ePS，i(t)，EPS，i(t-1) represents the energy of the pumped storage power station during the t and t-1 periods, respectively, EPS，i(T)，
Respectively representing the energy of the last period of the running simulation and the initial energy before the running simulation.
Respectively the charge and discharge efficiency of the pumped storage unit; en0Representing an initial value of the energy of the power station i pumped storage power station; qPS，i，
The number of units and the energy capacity which are respectively planned for the power station i,the number of the set and the upper limit value of the energy capacity which can be planned for the power station i are respectively. (9) (10) constraining the intermediate variable by a large M method, M being a constant guaranteed to be larger than the intermediate variable.
(3) Thermal power unit operation constraints
Constraints (19) - (22) are thermal power unit operation constraints, (19) are thermal power unit output upper and lower limit constraints, (20) are thermal power unit minimum continuous start-stop time constraints, (21) are thermal power unit climbing upper limit constraints, and (22) are thermal power unit maximum start-stop power constraints. In the formula ui，tA variable 0-1 representing the starting state of the node i unit at the time t, wherein 0 is shutdown 1 and is starting;respectively limiting the minimum output and the maximum output of the node i machine set; pG，i，tOutputting power for the node i unit at the time t;
the minimum continuous starting time limit value of the unit i is set;
the minimum continuous downtime limiting value of the unit i;
the unit climbing rate is a node i;
the unit downward climbing rate is a node i;
the maximum shutdown power of the unit is the node i;
and the maximum starting power of the point i unit.
(4) Outgoing line operational constraints
The energy base outgoing channel usually adopts a direct current transmission technology with strong transmission capacity. At present, a constant-power operation mode is adopted for the operation of a direct-current transmission line and cannot be changed frequently, and the mode is simply considered to be that the operation mode of a sending line is switched among a plurality of limited operation modes during modeling. Equations (23) - (24) constrain the outgoing dc link at node i at time t to operate in a mode of operation and the power delivered is the nominal power delivered in that mode. In the formula: u. ofln k.m，i，tFor the m-th operation mode state (decision variable) of the i-node line at the time t; pMode.m，iDelivering power for the mth mode of operation.
(5) Operation simulation system constraints
Storage removalThe method comprises the steps that in addition to an energy system, a thermal power generating unit and an external transmission line, grid frame constraint, system power balance constraint and rotation standby constraint need to be considered during system operation simulation. Constraints (25) - (27) are system operation simulation constraints, wherein (25) represents a node power balance constraint, (26) is a spinning standby constraint, and (27) is a line flow constraint. Wherein, PBS，m，tOutput power, P, for storing energy for m batteries of a unit at time tPS，i，tOutput power for pumped storage of group i for time period t, Fl，tRepresenting the transmission power on line 1 for a time period t, Fl maxUpper limit of power, theta, for line 1n，tRepresenting the phase angle of node n for period t.
Respectively are node incidence matrixes of thermal power generating units, wind power generating units, battery energy storage, pumped storage and delivery lines,
is a node line incidence matrix.
And step 3: constructing a solution model
And constructing a solving model according to the power grid parameters, the energy storage parameters and an energy storage planning model based on operation simulation, and setting the maximum calculation time and the allowable calculation error of the solving model.
And 4, step 4: scene setting and energy storage alternative node selection
For pumped storage, due to the restriction of hydraulic resources and geographic positions, when a pumped storage power station is built, a constructable position, namely an alternative node, is determined according to the investigation of a water conservancy department. The battery energy storage has no geographical position limitation, and can be configured at any node theoretically, but in actual planning, the distribution point of the energy storage is limited, so that the node with larger average energy storage requirement under each scene is identified by the solution model and is used as the alternative node. The specific settings of the scene are as follows:
1. the scenes are set according to the seasonal characteristics, namely, a typical day in each season corresponds to one scene. The load and new energy output curves in different seasons have large difference, and the line trend can be greatly changed under the influence, so that the energy storage requirements of the same node can have obvious difference in different seasons.
2. According to the energy storage cost of the battery
Setting a scene, namely, each group of battery energy storage cost corresponds to one scene. At the present stage, the battery energy storage technology is still in a rapid development stage, and the battery planning needs to consider the foresight of the battery energy storage technology and also consider the reality of higher cost at the present stage. Therefore, different setting scenarios according to the energy storage cost are necessary. The cost change of pumping and storing energy is small, so that extra scene setting is not made according to the cost change.
3. Punishment coefficient k according to abandoned windwSetting a scene, namely, each value of the wind curtailment coefficient corresponds to one scene. In consideration of the current strategic requirements for promoting wind power consumption, the wind curtailment penalty is considered in the objective function of the energy storage planning model based on the operation simulation, the preference degree of a decision maker for sacrificing the economy of the thermal power generating unit to promote the consumption of new energy is reflected by the wind curtailment penalty coefficient, and different wind curtailment penalty coefficients obviously influence the point arrangement constant volume of the energy storage planning.
Changing typical days, battery energy storage cost and wind abandon penalty coefficient in sequence to obtain a series of scenes, averaging the battery energy storage under each scene of each node, and taking the maximum first N according to the average valueoAnd each node is used as a battery energy storage alternative node.
And 5: calculating the energy storage demand ratio YrateAnd the equivalent maximum energy storage requirement day of the whole year
In order to integrate the energy storage requirements of wind power in all seasons, the invention provides an energy storage requirement ratio YrateAnd the equivalent maximum energy storage requirement day of the whole yearTwo indexes are provided. The difference between the load and the wind power in the power system is the largest in winter and summer, namely two typical days in winter and summerFor example, the energy storage investment of typical days in winter and summer is respectively calculated by using the solving model constructed in the step 3The total demand of stored energy in the season is reflected by the investment of stored energy, and the quotient of the two is the demand ratio of stored energy. Typical days N in winter and summer with large investment by stored energys1，Ns2As the maximum energy storage requirement day, the equivalent maximum energy storage requirement day of the whole yearCan be calculated according to equation (29).
Step 6: updating battery energy storage costs converted to daysPumped storage cost
The obtained annual equivalent maximum energy storage requirement day
Substitution of N in formula (1)dTo obtain updated battery energy storage cost
Pumped storage cost
Simultaneously adding N selected in the step 4oThe alternative nodes serve as input conditions for battery energy storage. Substituting the updated input parameters into the solution model in the step 3 for simulation calculation to obtainThe result is the final point distribution and volume fixing scheme.
Example 1
The embodiment 1 of the invention is based on an improved IEEE14 node system, the power grid parameters are kept unchanged, and two wind generating sets are additionally arranged in the system on the basis. Meanwhile, the system comprises two types of energy storage, namely battery energy storage and pumped storage. The method comprises the following steps:
1. and acquiring power grid parameters and energy storage parameters. The IEEE14 node system comprises 14 nodes, 6 conventional units and 20 branches, and the power grid parameters are as follows: the generator parameters, system load parameters, and branch parameters are shown in tables 1-3. The line-node association matrix A is generated by the serial numbers of the start node and the end node in the table 3, and the line admittance blThe inverse of the branch impedance in table 3. Line transport capacity Pl maxSee table 3. The spinning standby ratio R is set to 0.05. And selecting the No. 11 node as a node of a wind power delivery line of the system, wherein the delivery line is a direct current line, and the direct current line is set to deliver in two constant power operation modes of 90MW and 180M.
The total number of all thermal power generating units, the upper and lower output limits and the minimum continuous starting and stopping time of the thermal power generating units are shown in a table 1, the climbing rate of the thermal power generating units is 1% of the maximum output/min, the maximum starting and stopping power is set as the lower limit of the thermal power output, the units are started in the initial running state, and the initial running time is 24 hours;
200MW wind power is accessed to the No. 4 and No. 11 nodes, 200 yuan/MWh or 400 yuan/MWh is selected according to the requirements of different scenes, and the inter-area power transmission economic parameter is a line outgoing income coefficient koIs 340 yuan/MWh.
TABLE 1IEEE 14 node system set parameters
TABLE 2 System load parameters (static maximum load)
| Node point | load/MW | Node point | load/MW |
| 1 | 0 | 8 | 0 |
| 2 | 54.25 | 9 | 73.75 |
| 3 | 235.5 | 10 | 22.5 |
| 4 | 119.5 | 11 | 8.75 |
| 5 | 19 | 12 | 15.25 |
| 6 | 28 | 13 | 33.75 |
| 7 | 0 | 14 | 37.25 |
TABLE 3 System Branch parameters
Assuming that the load characteristics of each node in the system keep the same variation trend, that is, a unified per-unit load power curve is adopted, as shown in fig. 2(a) and fig. 3(a), the actual load power can be obtained by multiplying the maximum power of each node by the per-unit curve. The per-unit output curve of the wind power plant is obtained by per-unit converting the actual output of a certain wind power plant in northwest China according to the installed capacity of the wind power plant, as shown in fig. 2(b) and 3 (b).
In engineering practice, due to the restriction of hydraulic resources and geographic positions, when the pumped storage power station is built, firstly, an exploratory determination is performed to determine a constructable position, namely a planned access node, and therefore, in the system of the embodiment 1, it is assumed that the selected pumped storage power station building access node is node No. 7.
Unit power cost and unit energy cost of battery energy storage
Charge-discharge effect plug for energy storage of battery
As shown in table 4; the rated unit power of the battery energy storage is 5MW, the maximum continuous charging and discharging time is 3h, and the upper limit of the planned number of the energy storage units is 4.
Cost per unit power, cost per unit energy of pumped storage
Charge and discharge efficiency of pumped storage
As shown in the table 4 below, the following examples,the rated capacity of the pumped storage unit is set to be 20MW, and the upper limit of the energy capacity is set to be 160MWh due to the limitation of the storage capacity. The upper limit of the planned number of the pumped storage units of each node is 4.
TABLE 4 different energy storage parameters
TABLE 5 scenarios for different input conditions
2. And constructing a solving model according to the power grid parameters, the energy storage parameters and an energy storage planning model based on operation simulation, and setting the maximum calculation time of the solving model to be 10h and the allowable calculation error to be 0.1%.
3. Changing the typical day, the battery energy storage cost and the wind curtailment penalty coefficient in sequence can obtain a series of scenes, as shown in table 5.
4. And averaging the battery planning energy storage capacity of each node under each scene, and selecting the nodes with the energy storage average value larger than a threshold value of 3MW as battery energy storage alternative nodes. Therefore, the finally obtained battery energy storage alternative node is as follows: node 11, node 4, node 3, node 10.
Table 6 energy storage alternative nodes selected according to energy storage mean value
| Node numbering | 11 | 4 | 3 | 10 |
| Energy storage mean/MW | 13.1 | 6.3 | 4.4 | 3.1 |
5. Calculating the total cost of the configured energy storage in each scene, and calculating the energy storage demand ratio Y in typical days of winter and summerrateAnd the equivalent maximum energy storage requirement day of the whole year
The energy storage demand ratios of winter and summer corresponding to each input condition and the average value thereof are shown in Table 7, and the annual equivalent maximum energy storage demand day is obtained according to the formula (29)
It was 260 days.
TABLE 7 energy storage demand ratio of different input conditions for winter and summer
| Categories | Scene 1/scene 2 | Scene 3/scene 4 | |
Scene 7/scene 8 | Mean value |
| Energy storage demand ratio | 2.1 | 2.1 | 2.3 | 2.5 | 2.25 |
6. Using the annual equivalent maximum energy storage requirement day
Substitution of N in formula (1)dAnd (4) converting the investment cost by the updating date, and simultaneously using the alternative nodes selected in the step (3) as input conditions of battery energy storage. And (4) bringing the updated parameters into a simulation platform for calculation, wherein the obtained result is the final setting and constant volume scheme. Taking low energy storage cost and a wind curtailment penalty coefficient of 200 yuan/MWh as an example, the arrangement point and the volume fixing scheme are shown in Table 8. Comparing the operation conditions of the system before and after energy storage configuration, wherein the operation curve before energy storage configuration is shown in fig. 4, wind abandon occurs in 14 periods, and the peak value of the wind abandon power is close to 140 MW; meanwhile, the utilization rate of the outgoing line is low, and the rated outgoing power is reached in only 9 time periods. Fig. 5 is an operation curve after energy storage configuration, and the wind abandoning time period and the wind abandoning peak value are both obviously reduced, and the utilization rate of an outgoing line is improved. The comparison of the costs before and after the energy storage planning is shown in table 9.
TABLE 8 planning scheme for integrating characteristics of winter and summer season
TABLE 9 fees before and after energy storage planning
| Cost/ten thousand yuan | Running cost | Abandon wind | Battery with a battery cell | Pumping storage device | Delivery of drugs | General assembly |
| With stored energy | 89.9 | 5.0 | 7.7 | 3.8 | -119 | -12.9 |
| Without energy storage | 123.2 | 13.6 | 0 | 0 | -97.9 | 38.8 |
Example 2
The embodiment 2 of the invention is based on an improved IEEE39 node system, the power grid parameters are kept unchanged, and two wind generating sets are additionally arranged in the system on the basis. Meanwhile, the system comprises two types of energy storage, namely battery energy storage and pumped storage. The method comprises the following steps:
1. and acquiring power grid parameters and energy storage parameters. The IEEE39 node system comprises 39 nodes, 7 conventional units, 3 wind power units and 46 branches, and the power grid parameters are as follows: the generator parameters, system load parameters, and branch parameters are shown in tables 10-12. Line-node association matrix A composed ofSequence numbers of start node and end node in Table 12, line admittance blThe inverse of the branch impedance in table 12. Line transport capacity Fl maxSee table 12. The spinning standby ratio R is set to 0.05. And selecting the No. 11 node as a node of a wind power delivery line of the system, wherein the delivery line is a direct-current line, and the direct-current line is set to deliver in two constant-power operation modes of 320MW and 640M.
The total number of all thermal power generating units, the upper and lower output limits and the minimum continuous starting and stopping time of the thermal power generating units are shown in a table 10, the climbing rate of the thermal power generating units is 1% of the maximum output/min, the maximum starting and stopping power is set as the lower limit of the thermal power output, the units are started in the initial running state, and the initial running time is 24 hours;
the No. 4, No. 11 and No. 18 nodes are connected with 600MW wind power, the wind abandoning penalty coefficient is respectively selected to be 200 yuan/MWh or 400 yuan/MWh according to the requirements of different scenes, and the inter-area power transmission economic parameter is a line delivery income coefficient koIs 340 yuan/MWh.
TABLE 10IEEE 39 node system set parameters
TABLE 11 system Branch parameters
TABLE 12 System load parameters (static maximum load)
| Node point | load/MW | Node point | load/MW |
| 1 | 97.6 | 18 | 158 |
| 3 | 322 | 21 | 274 |
| 7 | 235.8 | 25 | 224 |
| 9 | 6.5 | 26 | 139 |
| 12 | 8.53 | 27 | 281 |
| 15 | 320 | 31 | 9.2 |
| 16 | 329 | —— | —— |
The load per unit power curves and the wind farm per unit output curves in typical days of winter and summer are shown in fig. 2(a), 2(b), 3(a) and 3 (b).
In engineering practice, due to the restriction of hydraulic resources and geographic positions, when the pumped storage power station is built, firstly, an exploratory determination is performed to determine a constructable position, namely a planned access node, and therefore, in the system of the embodiment 2, it is assumed that the selected pumped storage power station building access nodes are nodes 7 and 32.
Unit power cost and unit energy cost of battery energy storage
Charge-discharge efficiency of battery energy storage
As shown in table 4; the rated unit power of the battery energy storage is 5MW, the maximum continuous charging and discharging time is 3h, and the upper limit of the planned number of the energy storage units is 8.
Cost per unit power, cost per unit energy of pumped storage
Charge and discharge efficiency of pumped storageAs shown in table 4, the rated capacity of the pumped-storage unit is set to 50MW, and the upper limit of the energy capacity is set to 400MWh due to the reservoir capacity limitation. The upper limit of the planned number of the pumped storage units of each node is 4.
2. And constructing a solving model according to the power grid parameters, the energy storage parameters and an energy storage planning model based on operation simulation, and setting the maximum calculation time of the solving model to be 10h and the allowable calculation error to be 0.1%.
3. A series of scenes can be obtained by sequentially changing the typical day, the battery energy storage cost and the wind abandoning penalty coefficient, and the scene settings are as shown in the table 5.
4. And averaging the battery planning energy storage capacity of each node in each scene, and selecting the nodes with the average value larger than 3MW as battery energy storage alternative nodes. Therefore, the finally obtained battery energy storage alternative node is as follows: node 37, node 25, node 30, node 2.
Table 13 energy storage alternative nodes selected according to energy storage mean value
5. The total cost of energy storage configuration under each scene is calculated, and the energy storage demand ratio Y of typical days in winter and summer is calculatedrateAnd the equivalent maximum energy storage requirement day of the whole year
The energy storage demand ratios of winter and summer corresponding to the input conditions and the average value thereof are shown in Table 14, and the year-round equivalent maximum energy storage demand day is obtained according to the formula (29)
It was 273 days.
TABLE 14 energy storage demand ratio of winter and summer corresponding to different input conditions
| Categories | Scene 1/scene 2 | Scene 3/scene 4 | |
Scene 7/scene 8 | Mean value |
| Energy storage demand ratio | 2.1 | 2.1 | 2.2 | 1.5 | 2.0 |
6. Using the annual equivalent maximum energy storage requirement day
Substitution of N in formula (1)dAnd (4) converting the investment cost by the updating date, and simultaneously using the alternative nodes selected in the step (3) as input conditions of battery energy storage. And (4) substituting the updated parameters into a simulation platform for calculation to obtain a final planning scheme. Taking low energy storage cost and a wind curtailment penalty coefficient of 200 yuan/MWh as an example, the arrangement point and the volume fixing scheme are shown in Table 15. Comparing the operation conditions of the system before and after energy storage configuration, wherein the operation curve before energy storage configuration is shown in FIG. 6, and the number of wind abandoning periods and the wind abandoning power peak value are respectively 8 and 600 MW; meanwhile, the utilization rate of the outgoing line is low, and the rated outgoing power is reached in only 14 time periods. Fig. 7 is an operation curve after energy storage configuration, and the wind abandoning time period and the wind abandoning peak value are both significantly reduced, and the utilization rate of the outgoing line is improved. The comparison of the costs before and after the energy storage planning is shown in table 16.
TABLE 15 planning scheme for integrating characteristics of winter and summer season
TABLE 16 fees before and after energy storage planning
| Cost/ten thousand yuan | Running cost | Abandon wind | Battery with a battery cell | Pumping storage device | Delivery of drugs | General assembly |
| With stored energy | 719.6 | 0 | 22.1 | 54.4 | -413.4 | 382.7 |
| Without energy storage | 887.6 | 51.5 | 0 | 0 | -413.4 | 525.6 |
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (2)
1. An energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round is characterized by comprising the following steps:
(1) acquiring power grid parameters and energy storage parameters, and establishing an energy storage planning model based on operation simulation by taking the minimum sum of operation cost, energy storage investment, wind abandon punishment and delivery cost as a target function and taking constraint of a battery energy storage system, constraint of a pumped storage system, constraint of thermal power generating unit operation, constraint of delivery line operation and constraint of an operation simulation system as constraint conditions;
(2) constructing a solving model according to the power grid parameters, the energy storage parameters and an energy storage planning model based on operation simulation;
(3) changing typical days, battery energy storage cost and wind abandon penalty coefficients in sequence to obtain a series of scenes, averaging the battery energy storage values under each scene of each node, and obtaining the top N with the maximum average valueoEach node is a standby node for storing energy by a battery, and the standby node for pumping water and storing energy is a construction position;
(4) obtaining energy storage investment of a typical day in winter and energy storage investment of a typical day in summer by using a solution model, wherein the quotient of the energy storage investment of the typical day in winter and the energy storage investment of the typical day in summer is an energy storage requirement ratio, the typical day with larger energy storage investment is a maximum energy storage requirement day, and the energy storage requirement ratio is used for obtaining an equivalent maximum energy storage requirement day of the whole year;
(5) updating an energy storage planning model based on operation simulation by utilizing the annual equivalent maximum energy storage requirement day to obtain updated battery energy storage cost and pumped storage cost, and obtaining a final point distribution constant volume scheme according to the alternative node of battery energy storage, the alternative node of pumped storage, the updated battery energy storage cost and the pumped storage cost based on a solution model;
the energy storage parameters comprise technical parameters and economic parameters of battery energy storage, technical parameters and economic parameters of pumped storage, and the technical parameters and economic parameters of battery energy storage comprise: number N of alternative nodes for battery energy storage planningsUnit power cost, unit energy cost of battery energy storage
Charge-discharge efficiency of battery energy storage
Rated unit power of battery energy storage
According to the maximum continuous charging and discharging time H of rated power, the upper limit of the planned quantity of the battery energy storage units of each node
The technical parameters and economic parameters of pumped storage include: number of candidate nodes N for pumped storage planningpCost per unit power, cost per unit energy of pumped storage
Charge and discharge efficiency of pumped storage
Rated power of pumped storage unit
Upper limit of planned quantity of pumped storage units of each node
The objective function is:
minΨ＝Cgen+Cstr+Cwin-Cout，
wherein, CgenRepresents the running cost, CstrRepresents the energy storage investment, CwinDenotes a wind curtailment penalty, CoutThe benefit of the outgoing line is represented,
wherein T is the total time period number of the operation simulation, Np,Ns,NcThe total number of the pumped storage power station, the battery storage power station and the thermal power generating unit,
respectively representing the output and the running cost of the unit at the time period t of the i time period; SPt wThe total amount of the abandoned wind in the t-th time period; plnk,tThe delivered power of the t-th time period;
respectively planning the power capacity and the energy capacity of the mth battery energy storage power station; eEPS,iEnergy capacity, Q, for the ith pumped storage power stationPS,iThe number of the pumped storage groups of the ith pumped storage power station,
for the rated power, k, of pumped storage unitsoExpressing the coefficient of delivery revenue, delta the discount rate, YRsRepresenting the battery energy storage full life cycle, YRpIndicating pumped storage life cycle, NdThe number of days for equivalent utilization of energy storage in the whole year is shown,
unit power cost, energy cost for battery energy storage
The battery energy storage cost to date is converted according to the whole life cycle,
unit power cost and energy cost of pumped storage respectivelyAnd converting the pumped storage cost to the daily cost according to the whole life cycle of the pump storage system.
2. The energy storage, distribution and capacity measurement method integrating wind power annual multi-season characteristics as claimed in claim 1, wherein the grid parameters comprise: economic parameters of thermal power generating units, economic parameters of wind power generating units, economic parameters of power transmission among regions, technical parameters of systems and thermal power, and power data of wind power and loads in typical days of winter and summer
The technical parameters of the system comprise: line-node association matrix A, line admittance blRated operating power of outgoing lineLine transport capacity Fl maxThe system reserves a rotation standby ratio R for coping with the unit fault;
the thermal power technical parameters comprise: total number N of thermal power generating unitsgUpper and lower limits P of output of each thermal power generating unit ii max、Pi minUpward and downward climbing rate
Maximum start and stop power
Minimum continuous start-up and shut-down time Ti U,Ti DInitial running state and initial running time of unit
Ti 0；
The economic parameter of the thermal power generating unit is an operation cost secondary curve parameter ai,bi,ciThe wind turbine generator economic parameter is a wind abandon penalty coefficient kwThe inter-regional power transmission economic parameter is a line delivery income coefficient ko。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710974712.7A CN107732949B (en) | 2017-10-17 | 2017-10-17 | Energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710974712.7A CN107732949B (en) | 2017-10-17 | 2017-10-17 | Energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107732949A CN107732949A (en) | 2018-02-23 |
| CN107732949B true CN107732949B (en) | 2020-02-21 |
Family
ID=61212020
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710974712.7A Active CN107732949B (en) | 2017-10-17 | 2017-10-17 | Energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107732949B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108648096B (en) * | 2018-04-06 | 2022-03-18 | 东北电力大学 | Pumped storage power station capacity planning method based on life cycle cost theory |
| CN109347127B (en) * | 2018-10-27 | 2020-07-28 | 华中科技大学 | Energy storage optimal configuration method for dealing with regional power supply line faults |
| CN109687423B (en) * | 2018-12-29 | 2020-08-04 | 华中科技大学 | Energy storage configuration method of direct current power grid |
| CN110970912B (en) * | 2019-12-09 | 2023-08-04 | 国网新疆电力有限公司 | Operation simulation method for new energy power system containing stored energy |
| CN111799841B (en) * | 2020-07-31 | 2023-11-28 | 国网山东省电力公司经济技术研究院 | Combined decision method and system for thermal power generating unit flexibility transformation and power transmission planning |
| CN114069688B (en) * | 2020-08-07 | 2023-12-12 | 华北电力大学（保定） | Multi-power-supply capacity layout planning method based on time sequence production simulation |
| CN112234632B (en) * | 2020-09-30 | 2023-12-22 | 国网新疆电力有限公司经济技术研究院 | Seasonal hydrogen energy storage planning method |
| CN113255141B (en) * | 2021-05-31 | 2023-10-24 | 国网重庆市电力公司 | Method for calculating investment capacity and installation position of energy storage power station |
| CN114884101B (en) * | 2022-07-04 | 2022-09-30 | 华中科技大学 | Pumped storage dispatching method based on self-adaptive model control prediction |
| CN117318182B (en) * | 2023-11-28 | 2024-03-05 | 中国能源建设集团湖南省电力设计院有限公司 | Fire, wind and light storage integrated base capacity optimization configuration method |
-
2017
- 2017-10-17 CN CN201710974712.7A patent/CN107732949B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN107732949A (en) | 2018-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107732949B (en) | Energy storage, distribution and constant volume method integrating multi-season characteristics of wind power all year round | |
| CN106169108B (en) | Active power distribution network short-term active power optimization method containing battery energy storage system | |
| CN106981888B (en) | Wind based on multi-source complementation stores the multiple target dynamic dispatching method of Thermal and Hydroelectric Power Systems | |
| CN110365013B (en) | Capacity optimization method of photo-thermal-photovoltaic-wind power combined power generation system | |
| CN112069734A (en) | Optimal regulation and control method and system for electric heat pump-thermoelectric combined system | |
| CN109742796A (en) | A kind of Multiple Time Scales coordinated scheduling method of scene extreme misery storage association system | |
| CN110661301A (en) | Capacity allocation optimization method for water-light-storage multi-energy complementary power generation system | |
| CN113489003A (en) | Source network coordination planning method considering wind, light and water integrated complementary operation | |
| CN109615125B (en) | Multi-region random production simulation method considering extra-high voltage peak regulation and application | |
| CN117526446A (en) | Wind-solar capacity double-layer optimization configuration method for cascade water-wind-solar multi-energy complementary power generation system | |
| CN112307603A (en) | Hybrid energy storage capacity optimal configuration method and system considering large-scale wind power access | |
| CN109462255B (en) | Energy storage system-based power distribution network photovoltaic power generation capacity optimization method and system | |
| CN117114281A (en) | Determination method of flexible resource multi-stage planning scheme | |
| CN116742664A (en) | Short-term battery energy storage and seasonal hydrogen storage collaborative planning method and system | |
| CN116979578A (en) | Electric and thermal triple generation optimal scheduling method and system for wind, light, water and fire storage | |
| CN114094608B (en) | Photovoltaic power station-oriented multi-type energy storage capacity optimal configuration method and device | |
| Han et al. | Analysis of economic operation model for virtual power plants considering the uncertainties of renewable energy power generation | |
| CN114094609B (en) | Optimal configuration method of power grid energy storage system | |
| CN115907110A (en) | Power system flexibility resource assessment method and multi-dimensional scheduling method | |
| CN115085227A (en) | Micro-grid source storage capacity configuration method and device | |
| CN114583762A (en) | Daily power and electric quantity balance analysis method considering multi-partition thermal power balance scheduling | |
| CN114884101A (en) | Pumped storage dispatching method based on self-adaptive model control prediction | |
| CN114004390A (en) | Energy storage system capacity planning method considering wind power delivery and investment risk | |
| CN114722615A (en) | Energy storage capacity optimal configuration method based on production operation simulation | |
| CN114465226A (en) | Method for establishing multi-level standby acquisition joint optimization model of power 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 |