CN114039384A - Source-storage coordination optimization scheduling method based on new energy consumption - Google Patents
Source-storage coordination optimization scheduling method based on new energy consumption Download PDFInfo
- Publication number
- CN114039384A CN114039384A CN202111312422.9A CN202111312422A CN114039384A CN 114039384 A CN114039384 A CN 114039384A CN 202111312422 A CN202111312422 A CN 202111312422A CN 114039384 A CN114039384 A CN 114039384A
- Authority
- CN
- China
- Prior art keywords
- new energy
- power
- source
- optimization scheduling
- storage
- 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.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005265 energy consumption Methods 0.000 title claims abstract description 26
- 238000004146 energy storage Methods 0.000 claims abstract description 55
- 238000010248 power generation Methods 0.000 claims abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 230000005611 electricity Effects 0.000 claims description 5
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 4
- 238000007599 discharging Methods 0.000 abstract description 13
- 230000009194 climbing Effects 0.000 description 8
- 230000035772 mutation Effects 0.000 description 3
- YBJHBAHKTGYVGT-ZKWXMUAHSA-N (+)-Biotin Chemical compound N1C(=O)N[C@@H]2[C@H](CCCCC(=O)O)SC[C@@H]21 YBJHBAHKTGYVGT-ZKWXMUAHSA-N 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000005622 photoelectricity Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
- FEPMHVLSLDOMQC-UHFFFAOYSA-N virginiamycin-S1 Natural products CC1OC(=O)C(C=2C=CC=CC=2)NC(=O)C2CC(=O)CCN2C(=O)C(CC=2C=CC=CC=2)N(C)C(=O)C2CCCN2C(=O)C(CC)NC(=O)C1NC(=O)C1=NC=CC=C1O FEPMHVLSLDOMQC-UHFFFAOYSA-N 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- 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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a source-storage coordination optimization scheduling method based on new energy consumption. The method comprises the following steps: acquiring the forecast of the day-ahead load and the output of new energy of the power grid; generating a typical scene of the predicted day-ahead output of new energy; establishing an energy storage power station adjusting model; establishing a source-storage coordination optimization scheduling model; and solving the source-storage coordination optimization scheduling model. The invention provides a source-storage coordination optimization scheduling method based on new energy consumption, aiming at consuming new energy, and aiming at consuming a large-capacity battery energy storage power station to be scheduled, the peak regulation capacity of a system can be improved in a new energy high-power period, meanwhile, the energy storage power station serves as a load, the power generation power of the new energy which is not consumed is stored when the energy storage power station operates in a charging mode, the energy storage power station serves as a power supply when the new energy is low-power period, and the power is supplied to the load when the energy storage power station operates in a discharging mode, so that the consumption capacity of the new energy is improved.
Description
Technical Field
The invention belongs to the technical field of source-storage coordination optimization, and particularly relates to a source-storage coordination optimization scheduling method based on new energy consumption.
Background
The increase scale of the installed capacity of new energy in China is continuously enlarged, the new energy gradually replaces the traditional energy to become the leading energy, and the trend is inevitable, and a full renewable energy power generation system is likely to be built in the future. With the continuous increase of the installed capacity of new energy, the proportion of the new energy power generation in the power supply of the power grid is continuously improved, the challenge is brought to the operation of the power system, and higher requirements are provided for the evaluation of the new energy consumption capacity of the power grid, the formulation of a power grid dispatching plan and the like. The new energy power generation double peaks (night wind power peak, noon photovoltaic peak) and the power utilization double peaks (early peak, late peak) are staggered, and the contradiction between electricity abandonment at valley time and electricity shortage at peak time is faced, so that the consumption of new energy is greatly limited.
The energy storage power station can be used for power regulation of a long time scale, can store electric energy of wind power and photoelectricity in a peak period, and can deliver the electric energy to a valley period in a time-shifting mode, the action time can reach more than several hours, the advantages of strong climbing capability of an energy storage system, capability of positive and negative bidirectional regulation and the like are fully utilized, and the absorption capacity of a power grid to new energy is enlarged. With the continuous deepening of the research of the energy storage technology, the energy storage power station and the traditional power supply run coordinately to participate in scheduling, charge and discharge are carried out reasonably, and the transfer of electric energy in space and time can be realized, so that the consumption of new energy is promoted.
In the prior art, stored energy is mostly used for stabilizing the fluctuation of new energy and eliminating the uncertainty of wind power output, the consumption of wind power is mostly concentrated, the problem that the consumption of the new energy is blocked after photovoltaic grid connection is not considered, and the aim of minimizing the operation cost of a system is mostly taken, so that the new energy consumption capability under the dispatching mode can not be optimal. The method aims at the maximum consumption of the new energy, source-storage coordinated scheduling optimization is carried out, and the consumption of the new energy by the power system is improved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a source-storage coordinated optimization scheduling method based on new energy consumption, which aims at consuming new energy, performs source-storage coordinated scheduling optimization and improves the consumption of a power system to the new energy.
The source-storage coordination optimization scheduling method based on new energy consumption comprises the following steps:
s1: acquiring the forecast of the day-ahead load and the output of new energy of the power grid;
s2: generating a typical scene of the predicted day-ahead output of new energy;
s3: establishing an energy storage power station adjusting model;
s4: establishing a source-storage coordination optimization scheduling model;
s5: and solving the source-storage coordination optimization scheduling model.
The S1 includes the steps of:
s101: acquiring the day-ahead load prediction of a power grid;
s102: acquiring the predicted output of the wind power generation in the day ahead of the power grid;
s103: and acquiring the photovoltaic power generation predicted output of the power grid day ahead.
The S2 includes the steps of:
s201: generation of large amounts of wind-photovoltaic day-ahead predicted original scenes by Monte Carlo simulation
S202: for each scene wiCalculating the scene w with the shortest distance to itj；
S203: determining a scene w to deletei；
S204: modifying the scene number S to be S-1, and accumulating the deleted scene probability to the scene closest to the scene number S;
s205: repeating the steps until the number of the remaining scenes is equal to NS，NSThe number of desired reduced scene sets.
The S3 includes the steps of:
s301: establishing a charging power model of the energy storage power station;
s302: and establishing a charge state model of the energy storage power station.
The S4 includes the steps of:
s401: constructing an objective function with the maximum new energy consumption electric quantity as a target;
s402: establishing a system power balance constraint;
s403: establishing traditional thermal power generating unit operation constraints, including: the method comprises the following steps of (1) technical output constraint, unit climbing speed constraint and unit minimum start-stop time constraint of a traditional thermal power generating unit;
s404: establishing traditional hydroelectric generating set operation constraints, including: the output constraint and the climbing speed constraint of the traditional hydroelectric generating set.
S405: establishing wind power operation constraint;
s406: establishing photovoltaic operation constraints;
s407: and establishing the operation constraint of the energy storage power station. The method comprises the following steps: maximum charge-discharge power constraint, charge state constraint and charge-discharge balance constraint of the energy storage station;
the S5 includes the steps of:
s501: and solving a source-storage coordination optimization scheduling model by adopting an improved DESO algorithm, and outputting an energy storage day-ahead charging and discharging plan, a traditional power supply day-ahead output plan, a new energy source day-ahead output plan and new energy source consumption electric quantity W.
The invention discloses a source-storage coordination optimization scheduling method based on new energy consumption. The method comprises the following steps: acquiring the forecast of the day-ahead load and the output of new energy of the power grid; generating a typical scene of the predicted day-ahead output of new energy; establishing an energy storage power station adjusting model; establishing a source-storage coordination optimization scheduling model; and solving the source-storage coordination optimization scheduling model. The invention provides a source-storage coordination optimization scheduling method based on new energy consumption, aiming at consuming new energy, and aiming at reducing the consumption of new energy, by bringing a high-capacity battery energy storage power station into scheduling, the output of a traditional power supply can be reduced in a new energy high-power period, meanwhile, the energy storage power station serves as a load, when the energy storage power station operates in a charging mode, the electric energy which is not completely consumed by the load is stored, when the new energy low-power period, the energy storage power station serves as a power supply, and when the energy storage power station operates in a discharging mode, the power is supplied to the load, so that the consumption of the new energy is promoted.
Drawings
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
FIG. 1 is a flow chart of a source-storage coordination optimization scheduling method based on new energy consumption according to the present invention;
FIG. 2 is a graph of a load prediction before day as provided by the present invention;
FIG. 3 is a graph of predicted output of new energy sources in the past day according to the present invention;
FIG. 4 is a 10 typical scene graphs of the predicted error of the new energy day-ahead output provided by the present invention;
FIG. 5 is a day-ahead charging and discharging planning diagram for an energy storage power station provided by the present invention;
FIG. 6 is a day ahead force schedule for a conventional power supply provided by the present invention;
fig. 7 is a new energy day-ahead output planning chart before and after source-storage coordination optimization scheduling provided by the invention.
Detailed Description
In order to clearly understand the technical solution of the present invention, a detailed structure thereof will be set forth in the following description. It is apparent that the specific implementation of the embodiments of the present invention is not limited to the specific details familiar to those skilled in the art. Exemplary embodiments of the invention are described in detail below, and other embodiments in addition to those described in detail are possible.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
FIG. 1 is a flow chart of a source-reservoir coordination optimization scheduling method based on new energy consumption. In fig. 1, a flow chart of a source-storage coordination optimization scheduling method based on new energy consumption provided by the present invention includes:
s1: acquiring the forecast of the day-ahead load and the output of new energy of the power grid;
s2: generating a typical scene of the predicted day-ahead output of new energy;
s3: establishing an energy storage power station adjusting model;
s4: establishing a source-storage coordination optimization scheduling model;
s5: and solving the source-storage coordination optimization scheduling model.
The S1 includes the steps of:
s101: acquiring the day-ahead load prediction of a power grid;
s102: acquiring the predicted output of the wind power generation in the day ahead of the power grid;
s103: and acquiring the photovoltaic power generation predicted output of the power grid day ahead.
The S2 includes the steps of:
s201: generating a large number of wind, light and electricity day-ahead prediction original scenes through Monte Carlo simulation;
s202: for each scene wiCalculating the scene w with the shortest distance to itj：
Di，min＝minρjd(wi，wj)j≠i (1)
In the formula: rhojIs a scene wjThe occurrence probability of (2); d (w)i，wj) Is a scene wjAnd scene wjThe euclidean distance of (c).
S203: determining a scene w to deletei：
Dmin＝minρiDi，max (2)
S204: and modifying the scene number S-1, and accumulating the deleted scene probability to the scene closest to the scene.
S205: repeating the steps until the number of the remaining scenes is equal to NS，NSThe number of desired reduced scene sets.
The S3 includes the steps of:
s301: establishing a charging power model of the energy storage power station:
in the formula:
the active power of the energy storage power station in the time period t is equal
When the stored energy is in a charging mode, when
The time-storage energy is in a discharge mode; pEminStoring the maximum charging power, wherein the value of the maximum charging power is less than 0; pEmaxStoring the maximum discharge power;
respectively representing the charging power and the discharging power of the energy storage power station in a t period; etac、ηdRespectively representing the charging efficiency and the efficiency of the energy storage power station; mu.sc、μdThe state mark representing charging and discharging is 0 or 1, and cannot be 1 at the same time, namely the energy storage power station cannot work in the charging and discharging states at the same time.
S302: establishing a charge state model of the energy storage power station:
in the formula: SOC (t) and SOC (t-1) respectively represent the state of charge of the energy storage power station in a period t and a period t-1; Δ t represents a unit time; e represents the rated capacity of the energy storage power station; SOCminAnd SOCmaxRepresenting the upper and lower energy storage state of charge limits.
The S4 includes the steps of:
s401: constructing an objective function with the maximum new energy consumption electric quantity as a target:
in the formula: i is the time period number, TiThe total time period number is shown, delta t is the time length of each time period, K is the number of wind power photovoltaic operation scenes, N is the total number of units, s is the scene serial number, and pisIs the scene probability corresponding to the s-th operation scene, W is the new energy consumption electric quantity,
for the planned output of the wind turbine j at the time t,
and (4) the planned output of the photovoltaic power station j at the moment t.
S402: establishing a system power balance constraint:
in the formula:
the active power output of the traditional thermal power generating unit j at the moment t,
the active power output of the traditional hydroelectric generating set j at the moment t,
for the energy storage station j to have active power output at time t, PL(t) is the load power at time t.
S403: the method comprises the steps of establishing operation constraints of a traditional thermal power generating unit, and respectively establishing technical output constraints, unit climbing speed constraints and unit minimum start-stop time constraints of the traditional thermal power generating unit.
The technical output constraint of the traditional thermal power generating unit is as follows:
in the formula: pGj，maxAnd PGj，minRespectively setting the output upper limit and the output lower limit of the jth traditional thermal power generating unit;
and the running state of the unit j in the time period t is represented, 0 represents that the unit is not started, and 1 represents that the unit is running.
The climbing speed constraint of the traditional thermal power generating unit is as follows:
in the formula: pG，j，upAdjusting the power, P, of the unit j upwards in unit timeG，j，downThe power is adjusted for the unit j downward in unit time.
And the minimum start-stop time constraint of the traditional power supply unit is as follows:
in the formula:
the continuous starting time and the continuous stopping time of the thermal power generating unit j in the time period t are obtained;
and the lower limit of the continuous operation time and the continuous shutdown time of the thermal power generating unit j is obtained.
S404: and establishing the operation constraint of the traditional hydroelectric generating set, and respectively establishing the output constraint and the climbing speed constraint of the traditional hydroelectric generating set.
Output constraints of the traditional hydroelectric generating set are as follows:
in the formula: HPj，max、HPj，minThe upper limit and the lower limit of the output power of the hydroelectric generating set j are set.
The climbing speed constraint of the traditional hydroelectric generating set is as follows:
in the formula: HPj，up、HPj，downThe output limit values of j unit time interval rising and falling of the hydroelectric generating set are shown.
S405: establishing wind power operation constraint:
in the formula:
and predicting power of the wind turbine generator j day ahead.
S406: establishing photovoltaic operation constraints:
in the formula:
and predicting power for the photovoltaic power station j days ago.
S407: and establishing the operation constraint of the energy storage power station. And respectively establishing maximum charge and discharge power constraint, charge state constraint and charge and discharge balance constraint of the energy storage station.
The maximum charge and discharge power constraint of the energy storage power station is as follows:
in the formula:
representing the output of the energy storage station j in the time period t, at the moment
When the stored energy is in a charging mode, when
The time-storage energy is in a discharge mode; pE，jminStoring the maximum charging power, wherein the value of the maximum charging power is less than 0; pE，jmaxThe maximum discharge power is stored.
Secondly, the constraint of the state of charge of the energy storage power station is as follows:
SOCmin≤SOC(t)≤SOCmax (15)
in the formula: SOC (t) represents the state of charge of the stored energy over a period t; SOCminAnd SOCmaxRepresenting the upper and lower energy storage state of charge limits.
And thirdly, the charging and discharging balance constraint of the energy storage power station is as follows:
the S5 includes the steps of:
s501: and solving a source-storage coordination optimization scheduling model by adopting an improved DESO algorithm, and outputting an energy storage day-ahead charging and discharging plan, a traditional power supply day-ahead output plan, a new energy source day-ahead output plan and new energy source consumption electric quantity W. The DESO algorithm is a random parallel direct global search algorithm, has the advantages of simplicity and easiness in use in solving a nonlinear model, can ensure the effectiveness and the calculation efficiency of solution, but cannot ensure that a global optimal solution is accurately and timely found by a standard DESO algorithm when complex problems of high dimension and nonlinearity are processed, and is easy to fall into local optimal. Therefore, the model is solved by adopting a double mutation strategy based on population similarity and an improved DESO algorithm of adaptive cross probability. The double variation strategy ensures the diversity of the population, so that the model is not easy to fall into the local optimal solution; the self-adaptive cross probability can be self-adaptively adjusted according to the individual excellence, so that the population individuals can move to the individuals which are successfully updated, and the convergence of the algorithm is improved.
The method for solving the photo-thermal power generation capacity configuration model under different scenes by adopting the improved DESO algorithm comprises the following steps:
a. inputting DESO system parameters and algorithm parameters. The algorithm parameters include a maximum evolutionary algebra G, a population size MP, an individual dimension D, a scaling factor S and a cross probability CR. The system parameters comprise wind power photovoltaic day-ahead prediction data, traditional power supply adjusting parameters, energy storage power station adjusting performance parameters and the like.
b. And (5) initializing a population. And (3) producing an initialized population, wherein each individual in the population represents a group of control variables, including traditional power supply planned output, wind power plant planned output, photovoltaic power plant planned output and energy storage day-ahead charging and discharging plans.
c. And calculating the fitness. And calculating the fitness of each individual in the population, and selecting the individual with the optimal fitness.
d. The constraints are processed. And when the individual does not meet the constraint condition of the source-storage coordination day-ahead optimization scheduling model, modifying the fitness value of the individual to eliminate the individual.
e. Performing mutation operation by adopting double mutation strategy. And calculating the similarity of the population, and selecting a proper variation strategy according to the similarity of the current population.
f. And (4) crossing and selecting. And performing population crossing, and selecting a new generation of population from the population crossing.
g. And adaptively adjusting the cross probability. And carrying out self-adaptive adjustment on the cross probability according to the individual superiority, and reserving the cross probability of the superior individual to the next generation.
e. And c, repeating the steps c-g until the maximum evolution algebra is reached, and outputting an energy storage day-ahead charging and discharging plan, a traditional power supply day-ahead output plan, a new energy source day-ahead output plan and new energy source consumption electric quantity W.
Example 2
Taking an IEEE39 system as an example for analysis, the source-storage coordination optimization scheduling method based on new energy consumption provided by the invention comprises the following steps:
s1: obtaining power grid day-ahead load and new energy output prediction
A day-ahead load prediction curve is drawn according to a day-ahead load prediction acquired from the power grid, and is shown in fig. 2. And drawing a predicted output curve of the new energy in the day ahead according to the predicted output of the wind power photoelectric in the day ahead obtained from the power grid, wherein the predicted output curve of the new energy in the day ahead is shown in FIG. 3.
S2: generating a typical scene of new energy day-ahead output prediction
Firstly, generating a large number of wind, light and electricity output prediction scenes by a Monte Carlo simulation method, and then setting the number N of expected reduced scene setsS10, the wind power photoelectric original scene set is subtracted, and 10 typical scenes of the new energy day-ahead output prediction error are shown in fig. 4. Typical scene probabilities of the new energy output prediction errors before the day are shown in table 1.
TABLE 1 typical scene probability of new energy contribution prediction error before day
| Prediction error typical scenario | Probability% | Prediction error typical scenario | |
| Scene | |||
| 1 | 9.8 | Scene 6 | 5.2 |
| |
7.6 | Scene 7 | 12.4 |
| |
14.7 | Scene 8 | 10.2 |
| Scene 4 | 8.2 | Scene 9 | 9.3 |
| |
11.6 | |
9 |
S3: establishing energy storage power station regulation model
The installed capacity of the energy storage power station is 120MW/480MWh, the energy storage power station is a lithium iron phosphate battery energy storage power station, the rated power is 120MW, the rated capacity is 480MWh, and the state of charge SOC ismin＝0.2，SOCmaxThe maximum charge-discharge power is 120MW and the charge-discharge efficiency of the energy storage power station is 87%.
S4: establishing source-storage coordination optimization scheduling model
The total installed capacity of the conventional power supply in the local area is 1200MW, and the climbing speed is +/-1% of rated capacity. The conventional energy regulation parameter table is shown in table 2. The total installed capacity of wind power is 1200MW, and the total installed capacity of photovoltaic is 1000 MW.
TABLE 2 conventional power supply regulation parameter table
| Traditional power supply | Minimum technical output% | Maximum technical output% |
| Conventional thermal power generating unit | 50 | 100 |
| Conventional hydroelectric generating set (with regulation) | 0 | 100 |
| Conventional hydroelectric generating set (without regulation) | 100 | 100 |
S5: solving source-storage coordination optimization scheduling model
And solving a source-storage coordination optimization scheduling model by adopting an improved DESO algorithm, and outputting a day-ahead charging and discharging plan of the energy storage power station, a day-ahead output plan of a traditional power supply, a day-ahead output plan of new energy and the new energy consumption electric quantity W. The energy storage power station day-ahead charging and discharging plan is shown in fig. 5, the traditional power supply day-ahead output plan is shown in fig. 6, and the new energy day-ahead output plan before and after the source-storage coordination optimization scheduling is shown in fig. 7.
In fig. 7, the new energy blocked time periods are 00:00-06:00 and 10:00-15:00, the new energy is severely blocked before the source-storage coordinated optimization scheduling, the planned output of the new energy is far less than the predicted output of the new energy in the blocked time period, and after the source-storage coordinated optimization scheduling method is adopted, the planned output of the new energy blocked time period is greatly improved and slightly less than the predicted output of the new energy, so that the consumption of the electric power system on the new energy is improved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is set forth in the claims appended hereto.
Claims (6)
1. A source-storage coordination optimization scheduling method based on new energy consumption is characterized by comprising the following steps:
s1: acquiring the forecast of the day-ahead load and the output of new energy of the power grid;
s2: generating a typical scene of the predicted day-ahead output of new energy;
s3: establishing an energy storage power station adjusting model;
s4: establishing a source-storage coordination optimization scheduling model;
s5: and solving the source-storage coordination optimization scheduling model.
2. The new energy consumption based source-storage coordination optimization scheduling method according to claim 1, wherein said S1 comprises the following steps:
s101: acquiring the day-ahead load prediction of a power grid;
s102: acquiring the predicted output of the wind power generation in the day ahead of the power grid;
s103: and acquiring the photovoltaic power generation predicted output of the power grid day ahead.
3. The new energy consumption based source-storage coordination optimization scheduling method according to claim 1, wherein said S2 comprises the following steps:
s201: generating a large number of wind, light and electricity day-ahead prediction original scenes through Monte Carlo simulation;
s202: for each scene, calculating the scene with the shortest distance to the scene;
s203: determining a scene to be deleted;
s204: modifying the number of scenes, and accumulating the deleted scene probability to the scene closest to the deleted scene probability;
s205: and repeating the steps until the number of the remaining scenes is the number of the expected reduced scene sets.
4. The new energy consumption based source-storage coordination optimization scheduling method according to claim 1, wherein said S3 comprises the following steps:
s301: establishing a charging power model of the energy storage power station;
s302: and establishing a charge state model of the energy storage power station.
5. The new energy consumption based source-storage coordination optimization scheduling method according to claim 1, wherein said S4 comprises the following steps:
s401: constructing an objective function with the maximum new energy consumption electric quantity as a target;
s402: establishing a system power balance constraint;
s403: establishing operation constraints of a traditional thermal power generating unit;
s404: and establishing the operation constraint of the traditional hydroelectric generating set.
6. The new energy consumption based source-storage coordination optimization scheduling method according to claim 1, wherein said S5 comprises the following steps:
s501: and solving the source-storage coordination optimization scheduling model by adopting an improved DESO algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111312422.9A CN114039384A (en) | 2021-11-08 | 2021-11-08 | Source-storage coordination optimization scheduling method based on new energy consumption |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111312422.9A CN114039384A (en) | 2021-11-08 | 2021-11-08 | Source-storage coordination optimization scheduling method based on new energy consumption |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114039384A true CN114039384A (en) | 2022-02-11 |
Family
ID=80143195
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111312422.9A Pending CN114039384A (en) | 2021-11-08 | 2021-11-08 | Source-storage coordination optimization scheduling method based on new energy consumption |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114039384A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116205378A (en) * | 2023-04-28 | 2023-06-02 | 浙江中之杰智能系统有限公司 | Product scheduling management method and system based on block chain |
-
2021
- 2021-11-08 CN CN202111312422.9A patent/CN114039384A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116205378A (en) * | 2023-04-28 | 2023-06-02 | 浙江中之杰智能系统有限公司 | Product scheduling management method and system based on block chain |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111969593B (en) | Combined heat and power microgrid model prediction control optimization scheduling method based on hybrid energy storage | |
| CN111144620B (en) | Electric hydrogen comprehensive energy system considering seasonal hydrogen storage and robust planning method thereof | |
| CN107134810B (en) | Independent micro-energy-grid energy storage system optimal configuration solving method | |
| CN113962828B (en) | Comprehensive energy system coordination scheduling method considering carbon consumption | |
| CN111244988B (en) | Electric automobile considering distributed power supply and energy storage optimization scheduling method | |
| CN105225022A (en) | A kind of economy optimizing operation method of cogeneration of heat and power type micro-capacitance sensor | |
| CN112990523B (en) | Hierarchical optimization operation method for regional comprehensive energy system | |
| CN113159407B (en) | Multi-energy storage module capacity optimal configuration method based on regional comprehensive energy system | |
| CN110796373A (en) | Wind power consumption-oriented multi-stage scene generation electric heating system optimal scheduling method | |
| CN111325395A (en) | Multi-time scale source optimization scheduling method for photo-thermal power station to participate in adjustment | |
| CN113850474A (en) | Thermoelectric hydrogen multi-energy flow comprehensive energy system and optimal scheduling method thereof | |
| CN110889581A (en) | Electric vehicle-participated transformer area optimal scheduling method and system | |
| CN112736952A (en) | Calendar life-considered capacity optimization method for offshore wind power configuration energy storage system | |
| CN116613725A (en) | Photovoltaic power station direct-current hydrogen production optimal configuration method | |
| CN107834574B (en) | Control method for power exchange between distributed energy system and power grid | |
| CN113722903A (en) | Photo-thermal power generation capacity configuration method for full-renewable energy source sending-end system | |
| CN117081143A (en) | Method for promoting coordination and optimization operation of park comprehensive energy system for distributed photovoltaic on-site digestion | |
| CN116599148A (en) | Hydrogen-electricity hybrid energy storage two-stage collaborative planning method for new energy consumption | |
| CN115241923A (en) | Micro-grid multi-objective optimization configuration method based on snake optimization algorithm | |
| CN116050637A (en) | Comprehensive energy virtual power plant optimal scheduling method and system based on time-of-use electricity price | |
| CN115115130A (en) | Wind-solar energy storage hydrogen production system day-ahead scheduling method based on simulated annealing algorithm | |
| Hu et al. | Optimal dispatch of combined heat and power units based on particle swarm optimization with genetic algorithm | |
| CN115081700A (en) | Comprehensive energy storage technology-based data center multi-energy collaborative optimization method and system | |
| CN111144633A (en) | CCHP micro-grid operation optimization method | |
| CN114039384A (en) | Source-storage coordination optimization scheduling method based on new energy consumption |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |